{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#看看将数据集按年龄或者婚姻分类后预测结果\n",
    "# -*- coding: UTF-8 -*-\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.core.umath_tests\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "df1 = pd.read_csv('data/cleaned.csv')\n",
    "df1.sort_values(\"User_ID\",inplace=True)\n",
    "df1=df1.reset_index(drop=True)\n",
    "df1.drop(['Product_ID','Product_Category_1','Product_Category_2','Product_Category_3'], axis=1,inplace=True)\n",
    "tmp_df1 = df1[['User_ID','Purchase']]\n",
    "tmp_df2 = df1[['User_ID','Gender','Age','Occupation','City_Category','Stay_In_Current_City_Years','Marital_Status']]\n",
    "\n",
    "tmp_df = tmp_df1.groupby('User_ID').sum()\n",
    "tmp_df2=tmp_df2.drop_duplicates(['User_ID'])\n",
    "tmp_df2.sort_values(\"User_ID\",inplace=True)\n",
    "tmp_df2=tmp_df2.reset_index(drop=True)\n",
    "\n",
    "tmp_df=tmp_df.reset_index(drop=True)\n",
    "df = pd.concat([tmp_df2,tmp_df['Purchase']],axis=1)\n",
    "print(df['Purchase'].mean())\n",
    "'''\n",
    "df = pd.read_csv('data/cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User_ID                 int64\n",
      "Product_ID             object\n",
      "Gender                  int32\n",
      "Marital_Status          int64\n",
      "Product_Category_1      int64\n",
      "Product_Category_2    float64\n",
      "Product_Category_3    float64\n",
      "Purchase                int64\n",
      "0-17                    uint8\n",
      "18-25                   uint8\n",
      "26-35                   uint8\n",
      "36-45                   uint8\n",
      "46-50                   uint8\n",
      "51-55                   uint8\n",
      "55+                     uint8\n",
      "A                       uint8\n",
      "B                       uint8\n",
      "C                       uint8\n",
      "0                       uint8\n",
      "1                       uint8\n",
      "2                       uint8\n",
      "3                       uint8\n",
      "4+                      uint8\n",
      "0                       uint8\n",
      "1                       uint8\n",
      "2                       uint8\n",
      "3                       uint8\n",
      "4                       uint8\n",
      "5                       uint8\n",
      "6                       uint8\n",
      "7                       uint8\n",
      "8                       uint8\n",
      "9                       uint8\n",
      "10                      uint8\n",
      "11                      uint8\n",
      "12                      uint8\n",
      "13                      uint8\n",
      "14                      uint8\n",
      "15                      uint8\n",
      "16                      uint8\n",
      "17                      uint8\n",
      "18                      uint8\n",
      "19                      uint8\n",
      "20                      uint8\n",
      "dtype: object\n",
      "   User_ID Product_ID  Gender  Marital_Status  Product_Category_1  \\\n",
      "0        0  P00069042       0               0                   3   \n",
      "1        0  P00248942       0               0                   1   \n",
      "2        0  P00087842       0               0                  12   \n",
      "3        0  P00085442       0               0                  12   \n",
      "4        1  P00285442       1               0                   8   \n",
      "\n",
      "   Product_Category_2  Product_Category_3  Purchase  0-17  18-25 ...  11  12  \\\n",
      "0                 0.0                 0.0      8370     1      0 ...   0   0   \n",
      "1                 6.0                14.0     15200     1      0 ...   0   0   \n",
      "2                 0.0                 0.0      1422     1      0 ...   0   0   \n",
      "3                14.0                 0.0      1057     1      0 ...   0   0   \n",
      "4                 0.0                 0.0      7969     0      0 ...   0   0   \n",
      "\n",
      "   13  14  15  16  17  18  19  20  \n",
      "0   0   0   0   0   0   0   0   0  \n",
      "1   0   0   0   0   0   0   0   0  \n",
      "2   0   0   0   0   0   0   0   0  \n",
      "3   0   0   0   0   0   0   0   0  \n",
      "4   0   0   0   1   0   0   0   0  \n",
      "\n",
      "[5 rows x 44 columns]\n"
     ]
    }
   ],
   "source": [
    "#特征工程 解决编码问题\n",
    "#User_ID和Product_ID使用序号，Gender和Marray使用二元类型，其他使用热编码\n",
    "le_U_ID = LabelEncoder()\n",
    "df['User_ID'] = le_U_ID.fit_transform(df['User_ID'])\n",
    "le_P_ID = LabelEncoder()\n",
    "df['Gender'] = np.where(df['Gender']=='M',1,0) # Female: 0, Male: 1\n",
    "df_Age = pd.get_dummies(df.Age)\n",
    "df_CC = pd.get_dummies(df.City_Category)\n",
    "df_SIC = pd.get_dummies(df.Stay_In_Current_City_Years)\n",
    "df_encoded = pd.concat([df,df_Age,df_CC,df_SIC],axis=1)\n",
    "df_encoded.drop(['Age','City_Category','Stay_In_Current_City_Years'],axis=1,inplace=True)\n",
    "df_ocup = pd.get_dummies(df.Occupation)\n",
    "df_encoded = pd.concat([df_encoded,df_ocup],axis=1)\n",
    "df_encoded.drop(['Occupation'],axis=1,inplace=True)\n",
    "print(df_encoded.dtypes)\n",
    "print(df_encoded.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        User_ID Product_ID  Gender  Marital_Status  Product_Category_1  \\\n",
      "0             0  P00069042       0               0                   3   \n",
      "1             0  P00248942       0               0                   1   \n",
      "2             0  P00087842       0               0                  12   \n",
      "3             0  P00085442       0               0                  12   \n",
      "4             1  P00285442       1               0                   8   \n",
      "5             2  P00193542       1               0                   1   \n",
      "14            5  P00231342       0               0                   5   \n",
      "15            5  P00190242       0               0                   4   \n",
      "16            5   P0096642       0               0                   2   \n",
      "17            5  P00058442       0               0                   5   \n",
      "25            8  P00135742       1               0                   6   \n",
      "26            8  P00039942       1               0                   8   \n",
      "27            8  P00161442       1               0                   5   \n",
      "28            8  P00078742       1               0                   5   \n",
      "47           10  P00192642       0               0                   8   \n",
      "48           10  P00110842       0               0                   1   \n",
      "49           10  P00189642       0               0                   8   \n",
      "50           11  P00304242       1               0                   1   \n",
      "51           11  P00365242       1               0                   5   \n",
      "55           13  P00276642       1               0                   8   \n",
      "56           14  P00334242       1               0                   1   \n",
      "57           14  P00247542       1               0                   8   \n",
      "58           14  P00338442       1               0                   1   \n",
      "59           14  P00275142       1               0                   5   \n",
      "60           14  P00333042       1               0                   5   \n",
      "61           14  P00166242       1               0                   8   \n",
      "62           14  P00161942       1               0                   5   \n",
      "63           14  P00348242       1               0                   8   \n",
      "64           14  P00042142       1               0                   1   \n",
      "67           16  P00019342       1               0                   1   \n",
      "...         ...        ...     ...             ...                 ...   \n",
      "537494     4610  P00182042       1               0                  11   \n",
      "537495     4610   P0096742       1               0                   2   \n",
      "537496     4610  P00110542       1               0                   8   \n",
      "537497     4610  P00028842       1               0                   6   \n",
      "537498     4610  P00122442       1               0                   1   \n",
      "537499     4610  P00351142       1               0                   1   \n",
      "537500     4610  P00115842       1               0                  16   \n",
      "537501     4610  P00187842       1               0                   1   \n",
      "537502     4610  P00210342       1               0                   3   \n",
      "537503     4610  P00021942       1               0                   8   \n",
      "537504     4610  P00214842       1               0                  14   \n",
      "537505     4610  P00057242       1               0                   5   \n",
      "537506     4610  P00147842       1               0                   8   \n",
      "537507     4610  P00016342       1               0                   1   \n",
      "537508     4610  P00214642       1               0                  11   \n",
      "537545     4615  P00144042       1               0                   2   \n",
      "537546     4615  P00070042       1               0                   1   \n",
      "537547     4615  P00244042       1               0                   1   \n",
      "537550     4617  P00278242       1               0                   1   \n",
      "537551     4617  P00313442       1               0                   5   \n",
      "537552     4617   P0098642       1               0                   6   \n",
      "537553     4617  P00119342       1               0                  10   \n",
      "537554     4617  P00114042       1               0                   5   \n",
      "537555     4617  P00135142       1               0                  13   \n",
      "537571     4619  P00221442       1               0                   1   \n",
      "537572     4619  P00193542       1               0                   1   \n",
      "537573     4619  P00111142       1               0                   1   \n",
      "537574     4619  P00345942       1               0                   8   \n",
      "537575     4619  P00285842       1               0                   5   \n",
      "537576     4619  P00118242       1               0                   5   \n",
      "\n",
      "        Product_Category_2  Product_Category_3  Purchase  0-17  18-25 ...  11  \\\n",
      "0                      0.0                 0.0      8370     1      0 ...   0   \n",
      "1                      6.0                14.0     15200     1      0 ...   0   \n",
      "2                      0.0                 0.0      1422     1      0 ...   0   \n",
      "3                     14.0                 0.0      1057     1      0 ...   0   \n",
      "4                      0.0                 0.0      7969     0      0 ...   0   \n",
      "5                      2.0                 0.0     15227     0      0 ...   0   \n",
      "14                     8.0                14.0      5378     0      0 ...   0   \n",
      "15                     5.0                 0.0      2079     0      0 ...   0   \n",
      "16                     3.0                 4.0     13055     0      0 ...   0   \n",
      "17                    14.0                 0.0      8851     0      0 ...   0   \n",
      "25                     8.0                 0.0     16662     0      0 ...   0   \n",
      "26                     0.0                 0.0      5887     0      0 ...   0   \n",
      "27                    14.0                 0.0      6973     0      0 ...   0   \n",
      "28                     8.0                14.0      5391     0      0 ...   0   \n",
      "47                    17.0                 0.0      6171     0      0 ...   0   \n",
      "48                     2.0                 5.0     19327     0      0 ...   0   \n",
      "49                    13.0                 0.0      8027     0      0 ...   0   \n",
      "50                     6.0                 0.0     15246     0      0 ...   0   \n",
      "51                     8.0                 0.0      6865     0      0 ...   0   \n",
      "55                    11.0                 0.0      5848     0      0 ...   0   \n",
      "56                     8.0                 0.0     19653     0      0 ...   0   \n",
      "57                    16.0                 0.0      5958     0      0 ...   0   \n",
      "58                    16.0                 0.0     11415     0      0 ...   0   \n",
      "59                     8.0                 0.0      5380     0      0 ...   0   \n",
      "60                     8.0                 0.0      3594     0      0 ...   0   \n",
      "61                     0.0                 0.0      4209     0      0 ...   0   \n",
      "62                     8.0                 0.0      5407     0      0 ...   0   \n",
      "63                     0.0                 0.0      7803     0      0 ...   0   \n",
      "64                     2.0                 6.0     11458     0      0 ...   0   \n",
      "67                     6.0                15.0     15872     0      0 ...   0   \n",
      "...                    ...                 ...       ...   ...    ... ...  ..   \n",
      "537494                 0.0                 0.0      4418     0      0 ...   0   \n",
      "537495                 4.0                12.0     13115     0      0 ...   0   \n",
      "537496                 0.0                 0.0      9732     0      0 ...   0   \n",
      "537497                 8.0                 0.0     16317     0      0 ...   0   \n",
      "537498                11.0                15.0     11802     0      0 ...   0   \n",
      "537499                 8.0                17.0     11610     0      0 ...   0   \n",
      "537500                 0.0                 0.0     20269     0      0 ...   0   \n",
      "537501                16.0                 0.0     11909     0      0 ...   0   \n",
      "537502                 4.0                 0.0      8432     0      0 ...   0   \n",
      "537503                16.0                 0.0      6043     0      0 ...   0   \n",
      "537504                 0.0                 0.0     11013     0      0 ...   0   \n",
      "537505                 0.0                 0.0      6888     0      0 ...   0   \n",
      "537506                 0.0                 0.0      5955     0      0 ...   0   \n",
      "537507                 2.0                 8.0      7604     0      0 ...   0   \n",
      "537508                13.0                16.0      4683     0      0 ...   0   \n",
      "537545                 3.0                 4.0      9687     0      1 ...   0   \n",
      "537546                 2.0                16.0     15515     0      1 ...   0   \n",
      "537547                 2.0                15.0     11543     0      1 ...   0   \n",
      "537550                 0.0                 0.0     11658     0      0 ...   0   \n",
      "537551                 6.0                 8.0      6863     0      0 ...   0   \n",
      "537552                 8.0                 0.0     16415     0      0 ...   0   \n",
      "537553                13.0                 0.0     18526     0      0 ...   0   \n",
      "537554                14.0                 0.0      7099     0      0 ...   0   \n",
      "537555                16.0                 0.0       578     0      0 ...   0   \n",
      "537571                 2.0                 5.0     11852     0      0 ...   0   \n",
      "537572                 2.0                 0.0     11664     0      0 ...   0   \n",
      "537573                15.0                16.0     19196     0      0 ...   0   \n",
      "537574                15.0                 0.0      8043     0      0 ...   0   \n",
      "537575                 0.0                 0.0      7172     0      0 ...   0   \n",
      "537576                 8.0                 0.0      6875     0      0 ...   0   \n",
      "\n",
      "        12  13  14  15  16  17  18  19  20  \n",
      "0        0   0   0   0   0   0   0   0   0  \n",
      "1        0   0   0   0   0   0   0   0   0  \n",
      "2        0   0   0   0   0   0   0   0   0  \n",
      "3        0   0   0   0   0   0   0   0   0  \n",
      "4        0   0   0   0   1   0   0   0   0  \n",
      "5        0   0   0   1   0   0   0   0   0  \n",
      "14       0   0   0   0   0   0   0   0   0  \n",
      "15       0   0   0   0   0   0   0   0   0  \n",
      "16       0   0   0   0   0   0   0   0   0  \n",
      "17       0   0   0   0   0   0   0   0   0  \n",
      "25       0   0   0   0   0   1   0   0   0  \n",
      "26       0   0   0   0   0   1   0   0   0  \n",
      "27       0   0   0   0   0   1   0   0   0  \n",
      "28       0   0   0   0   0   1   0   0   0  \n",
      "47       0   0   0   0   0   0   0   0   0  \n",
      "48       0   0   0   0   0   0   0   0   0  \n",
      "49       0   0   0   0   0   0   0   0   0  \n",
      "50       1   0   0   0   0   0   0   0   0  \n",
      "51       1   0   0   0   0   0   0   0   0  \n",
      "55       0   0   0   0   0   0   0   0   0  \n",
      "56       0   0   0   0   0   0   0   0   0  \n",
      "57       0   0   0   0   0   0   0   0   0  \n",
      "58       0   0   0   0   0   0   0   0   0  \n",
      "59       0   0   0   0   0   0   0   0   0  \n",
      "60       0   0   0   0   0   0   0   0   0  \n",
      "61       0   0   0   0   0   0   0   0   0  \n",
      "62       0   0   0   0   0   0   0   0   0  \n",
      "63       0   0   0   0   0   0   0   0   0  \n",
      "64       0   0   0   0   0   0   0   0   0  \n",
      "67       0   0   0   0   0   0   0   0   0  \n",
      "...     ..  ..  ..  ..  ..  ..  ..  ..  ..  \n",
      "537494   0   0   0   0   0   0   0   1   0  \n",
      "537495   0   0   0   0   0   0   0   1   0  \n",
      "537496   0   0   0   0   0   0   0   1   0  \n",
      "537497   0   0   0   0   0   0   0   1   0  \n",
      "537498   0   0   0   0   0   0   0   1   0  \n",
      "537499   0   0   0   0   0   0   0   1   0  \n",
      "537500   0   0   0   0   0   0   0   1   0  \n",
      "537501   0   0   0   0   0   0   0   1   0  \n",
      "537502   0   0   0   0   0   0   0   1   0  \n",
      "537503   0   0   0   0   0   0   0   1   0  \n",
      "537504   0   0   0   0   0   0   0   1   0  \n",
      "537505   0   0   0   0   0   0   0   1   0  \n",
      "537506   0   0   0   0   0   0   0   1   0  \n",
      "537507   0   0   0   0   0   0   0   1   0  \n",
      "537508   0   0   0   0   0   0   0   1   0  \n",
      "537545   0   0   0   0   0   0   1   0   0  \n",
      "537546   0   0   0   0   0   0   1   0   0  \n",
      "537547   0   0   0   0   0   0   1   0   0  \n",
      "537550   0   0   0   0   0   0   0   0   0  \n",
      "537551   0   0   0   0   0   0   0   0   0  \n",
      "537552   0   0   0   0   0   0   0   0   0  \n",
      "537553   0   0   0   0   0   0   0   0   0  \n",
      "537554   0   0   0   0   0   0   0   0   0  \n",
      "537555   0   0   0   0   0   0   0   0   0  \n",
      "537571   0   0   0   0   1   0   0   0   0  \n",
      "537572   0   0   0   0   1   0   0   0   0  \n",
      "537573   0   0   0   0   1   0   0   0   0  \n",
      "537574   0   0   0   0   1   0   0   0   0  \n",
      "537575   0   0   0   0   1   0   0   0   0  \n",
      "537576   0   0   0   0   1   0   0   0   0  \n",
      "\n",
      "[317817 rows x 44 columns]\n"
     ]
    }
   ],
   "source": [
    "df_Female=df_encoded[df_encoded['Gender']==1]\n",
    "df_Male=df_encoded[df_encoded['Gender']==0]\n",
    "df_Marry=df_encoded[df_encoded['Marital_Status']==1]\n",
    "df_Unmarry=df_encoded[df_encoded['Marital_Status']==0]\n",
    "print(df_Unmarry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_frac_Male = df_Male.sample(frac=0.02,random_state=100)\n",
    "#X_Male = df_frac_Male.drop(['Purchase','User_ID'], axis=1)\n",
    "X_Male = df_frac_Male.drop(['Purchase','User_ID','Product_ID','Product_Category_1','Product_Category_2','Product_Category_3'], axis=1)\n",
    "y_Male = df_frac_Male['Purchase']\n",
    "X_train_Male,X_test_Male,y_train_Male,y_test_Male = train_test_split(X_Male,y_Male,random_state=100)\n",
    "\n",
    "df_frac_Female = df_Female.sample(frac=0.02,random_state=100)\n",
    "#X_Female = df_frac_Female.drop(['Purchase','User_ID'], axis=1)\n",
    "X_Female = df_frac_Female.drop(['Purchase','User_ID','Product_ID','Product_Category_1','Product_Category_2','Product_Category_3'], axis=1)\n",
    "y_Female = df_frac_Female['Purchase']\n",
    "X_train_Female,X_test_Female,y_train_Female,y_test_Female = train_test_split(X_Female,y_Female,random_state=100)\n",
    "\n",
    "df_frac_Marry = df_Marry.sample(frac=0.02,random_state=100)\n",
    "#X_Marry = df_frac_Marry.drop(['Purchase','User_ID'], axis=1)\n",
    "X_Marry = df_frac_Marry.drop(['Purchase','User_ID','Product_ID','Product_Category_1','Product_Category_2','Product_Category_3'], axis=1)\n",
    "y_Marry = df_frac_Marry['Purchase']\n",
    "X_train_Marry,X_test_Marry,y_train_Marry,y_test_Marry = train_test_split(X_Marry,y_Marry,random_state=100)\n",
    "\n",
    "df_frac_Unmarry = df_Unmarry.sample(frac=0.02,random_state=100)\n",
    "#X_Unmarry = df_frac_Unmarry.drop(['Purchase','User_ID'], axis=1)\n",
    "X_Unmarry = df_frac_Unmarry.drop(['Purchase','User_ID','Product_ID','Product_Category_1','Product_Category_2','Product_Category_3'], axis=1)\n",
    "y_Unmarry = df_frac_Unmarry['Purchase']\n",
    "X_train_Unmarry,X_test_Unmarry,y_train_Unmarry,y_test_Unmarry = train_test_split(X_Unmarry,y_Unmarry,random_state=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'n_estimators':[1,3,10,30,100,150,300,400,500],'max_depth':[1,3,5,7,9,11]}\n",
    "grid_rf_Male = GridSearchCV(RandomForestRegressor(),param_grid,cv=10,scoring='neg_mean_squared_error').fit(X_train_Male,y_train_Male)\n",
    "grid_rf_Female = GridSearchCV(RandomForestRegressor(),param_grid,cv=10,scoring='neg_mean_squared_error').fit(X_train_Female,y_train_Female)\n",
    "grid_rf_Marry = GridSearchCV(RandomForestRegressor(),param_grid,cv=10,scoring='neg_mean_squared_error').fit(X_train_Marry,y_train_Marry)\n",
    "grid_rf_Unmarry = GridSearchCV(RandomForestRegressor(),param_grid,cv=10,scoring='neg_mean_squared_error').fit(X_train_Unmarry,y_train_Unmarry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male Best parameter: {'max_depth': 1, 'n_estimators': 10}\n",
      "Male Best score: 4751.52\n",
      "Female Best parameter: {'max_depth': 7, 'n_estimators': 30}\n",
      "Female Best score: 4996.71\n",
      "Marry Best parameter: {'max_depth': 3, 'n_estimators': 10}\n",
      "Marry Best score: 4940.89\n",
      "Unmarry Best parameter: {'max_depth': 5, 'n_estimators': 300}\n",
      "Unmarry Best score: 4952.79\n"
     ]
    }
   ],
   "source": [
    "print('Male Best parameter: {}'.format(grid_rf_Male.best_params_))\n",
    "print('Male Best score: {:.2f}'.format((-1*grid_rf_Male.best_score_)**0.5))\n",
    "\n",
    "print('Female Best parameter: {}'.format(grid_rf_Female.best_params_))\n",
    "print('Female Best score: {:.2f}'.format((-1*grid_rf_Female.best_score_)**0.5))\n",
    "\n",
    "print('Marry Best parameter: {}'.format(grid_rf_Marry.best_params_))\n",
    "print('Marry Best score: {:.2f}'.format((-1*grid_rf_Marry.best_score_)**0.5))\n",
    "\n",
    "print('Unmarry Best parameter: {}'.format(grid_rf_Unmarry.best_params_))\n",
    "print('Unmarry Best score: {:.2f}'.format((-1*grid_rf_Unmarry.best_score_)**0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sizes_Male, train_scores_Male, valid_scores_Male = learning_curve(RandomForestRegressor(max_depth=1, n_estimators=10), X_train_Male, y_train_Male, cv=10, scoring='neg_mean_squared_error')\n",
    "\n",
    "train_sizes_Female, train_scores_Female, valid_scores_Female = learning_curve(RandomForestRegressor(max_depth=7, n_estimators=30), X_train_Female, y_train_Female, cv=10, scoring='neg_mean_squared_error')\n",
    "\n",
    "train_sizes_Marry, train_scores_Marry, valid_scores_Marry = learning_curve(RandomForestRegressor(max_depth=3, n_estimators=10), X_train_Marry, y_train_Marry, cv=10, scoring='neg_mean_squared_error')\n",
    "\n",
    "train_sizes_Unmarry, train_scores_Unmarry, valid_scores_Unmarry = learning_curve(RandomForestRegressor(max_depth=5, n_estimators=300), X_train_Unmarry, y_train_Unmarry, cv=10, scoring='neg_mean_squared_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f2985c54a8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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amO7VwnTdm7dk6N7j77//Gy64YHkhzLwaFoOaBvuD6+RjM9FsuHPnTi655JJRz9m9e/eMrKx7smQyOezrhkIhdF0nFAoRCoUK5zz11FP827/9GzfccANXXnklH330UeHzzTAMIpEIhmFQUVFBJBIhHA6jadqo783v9w/a2jebzU7qS3jJwqSvr4/HHnuM7373u1iWxbPPPsu999477LkXXXQRH330EQcPHixU5T796U/T1NREIBAo/M+wY8cOrrjiilIVWYhZp+U/dEGjuBfK79MH7YUz3YqDqdAE6Lg4NoUFRVW+1uXmN3dTrhdiB09kcQPt+XrXcG+q/0UGfmhaf82NQmDputd0WDw4w+v/0tE1b9kgrb82Vgi/fM1N1/O1rv7allaoccVTDl3xzJDH9Xw1TdcoNH8Wl3nQUJIZbn40TXPYkas/+clP+MM//EPWrVvHr3/9a95//31c152Rza/GUrIwueqqq3jnnXf41Kc+heu63HjjjYOavYoFAgEefvhh/uIv/oJsNsvq1av55Cc/CcAjjzzCPffcQyKR4Pzzz2fjxo2lKrIQZ6ziEJuo7rAxpAY/XoUQY6CPS8Ggfq6B5sT+cweeV7jGcO8p//PQ0TQZ43jRUTX4BAX1YYe+VG5cZR7IlXyTZXHwaEUNmSMEUvFzbMfbw2jweVBeEaOhsZEtW+5EKYVtu6DBzbds5Gv33cvjjz9OWVkZK1as4NChQ8xfsCA/aEUV/a5mdgi+pmb6FWdIf1VtKs1cb70nzVzTaS6XDeZ2+aRskzee8vmdDs4+e9mIj6vh2gYZ5pAa7ubgg8Xn53I5/H7/qGWbLE3TKAv7xqxV7d69m3PPPbdwf7KfnTIDXgghxlBc2xj62Eh3Rj0IgJ3vfyoF2xm+B61UZr+hTQghxClPwkQIIWDG+xjmgul8zxImQgiBQTabme1CzDjLskacsjFREiZCiDNejhDHjh0lk0mfMTUU13VpbW2loqJiWq4nHfBCCGEESTtw+PBRwJmxl7UtC9NXmvlDrqsI+k1GG8wViUSoqamZlteTMBFCCAAjSI7gjL7k3o9KN6y6I5HmkxcunLHFSaWZSwghxJRJmAghhJgyCRMhhBBTJmEihBBiyiRMhBBCTJmEiRBCiCmTMBFCCDFlEiZCCCGmTMJkBI7rnjHLKgghxFTJDPhhuK5i0/Yfk7McWurLaK6L0lwXZV5NdLaLJoQQc5KEyTB0XeNPrj2PH/z0IO09aT483AN422nGIgaL2z6iqc4LmcqywIzvDy2EEHONhMkIfmtZHbbtUl0RIp21OdqW4Eh7gj0H2nh3bwdvv98GQCRo0lQXpbnWC5d5tRF8pjHLpRdCiJklYTIOoYDJkpYYS1piNJdnOOusxbT3pDnS1seRtgRH2hLsOTRQe6mvCtNSV+aFTF2UWFRqL0KI05uEySToukZ9VZj6qjCXnFMPQCpjcbQ9UQiXX33Yzs93twIQCfloro0W+l4aa6L4TBn7IIQ4fZQ0TG655Ra6uroKO3ndd999HDp0iG9/+9vYts3nPvc5brrpJgD+8z//k4ceeohsNsuaNWvYtGkTALt37+buu+8mmUyycuVK7r333mnbGWw6hYM+zm6p5OyWSsDrxG/rThXC5Wh7Hx8c6gZA1zTqq8P5cPGaxyoifqm9CCFOWSX7VFZKceDAAV577bXCh39rayubNm3imWeewe/380d/9EdcfvnlNDc3c9ddd/HUU0/R2NjIF7/4Rd544w1Wr17N5s2buf/++1mxYgV33XUXTz/9NDfeeGOpij1tdF2joTpCQ3WEled6tZdkurj20sev9rTz8994tZdoyDcoXBqrI5hSexFCnCJKFib79+8H4NZbb6Wnp4cbbriBSCTCxz72MWKxGADXXHMNr7zyCpdddhkLFiygpaUFgHXr1vHKK6+wZMkSMpkMK1asAGDDhg089thjp0SYDCcS8rF0fiVL5w/UXlq7Uxxp7eNIe4KjbQneP5ivvegaDVXhQrg010WpiAZms/hCCDGikoVJPB5n1apVfOUrX8GyLDZu3MiaNWuora0tnFNXV8e7775LW1vbkOOtra1DjtfW1tLa2lqqIs84XddorI7QWB3h0vyxRNriaH/HfnuCX3zQxlu/OQFAWdg3KFwaqiOYhtRehBCzr2RhcvHFF3PxxRcX7n/mM5/hoYce4s/+7M8Kx5RSaJqG67qD+gvGOj4Ru3btmlT54ymHg0dTdIeG/or27t07qWuOlwEsqIQFlX7cJT56kg4dcYuOuM2h4z3sPtAFgK5BZdSkpnzgTzhglLx8UzGXywZzu3xStsmby+UrVdniKYed/k70GeqLLVmYvP3221iWxapVqwAvCJqammhvby+c097eTl1dHQ0NDeM63tHRQV1d3YTKsXz5cgKBiTcPdfamyRgnqK4IDTq+d2/p9mwer0QqV6i5HGlLsO9Egg+OZgAI+3UWzIvRUu/NfWmoDmPMkdrLXPjdjWYul0/KNnlzuXylLFtHb5pLfmvie8Bns9lJfQkvWZj09fXx2GOP8d3vfhfLsnj22Wf5+te/zubNm+nq6iIUCvGDH/yAr33tayxbtoyPPvqIgwcP0tzczIsvvsinP/1pmpqaCAQC7Ny5k0suuYQdO3ZwxRVXlKrIp4xo2M85C6s4Z2EVAI7j0tqV4nBbgvf3H+dYe6JQezEMrymtuHO/LOyfzeILIU5DJQuTq666infeeYdPfepTuK7LjTfeyCWXXMKmTZvYuHEjlmXxmc98hgsvvBCAhx9+mL/4i78gm82yevVqPvnJTwLwyCOPcM8995BIJDj//PPZuHFjqYp8yjIMnXm1UebVRqkOJFiyZAl9/bWXfP/Lz3e38tNdXt9LRcSfn1DphUtD1dypvQghTk0lnbBxxx13cMcddww6tm7dOtatWzfk3FWrVvH8888POX7OOefwL//yLyUr4+mqLOzn3IVVnJuvvdiOS2tnisNFs/Z/85FXezENjcaa/KTK/OTKqNRehBATMPdm/4mSMA2dprooTXUDKx/Hk9lCsBxpS/DWeyd40/WW3Y9FA4XlYJrrotRXhTF0qb0IIYYnYXIGK48EOG9RgPMWVQNg2y4nupIcaUtwuK2PQyfivLe/E/DCaF5NhKa6KC35JrJIyDebxRdCzCESJqLANPV8P0oZH6MRpRTxZK6o9tLHz947wZu/9movlWX52kutFy5lYR8+U8dnGhMeQSKEOLVJmIgRaZpGRTRARTTA+Wd5tRfLdjnRmSyEy4FjcXbt6xzyXEPX8sHi/TENA9fJ8dMPd2MWjhuDzvGZOj5jhOPDHJNmNyHmDgkTMSE+U6elvoyW+jLI1156EzmOtidIZ20s28VyXCzbwbZdLNsll/8Zj1vYjks6Z5OzXGzHO27ZDrYz8S2SdU0bJnQGgsc0dPy+gYAyi87xmzpmf0AZ3rHuhE1nb+akwNJkAU4hxkHCREyJpmnEygLEysaeGDraBC2lVD5YBgLGctyhx+yTjw0+186HVyaVK9wvvs6YfvHOSe8PL2x8RiF0RqwxGXohsPz9gTYowIoCzdAxDB1d80JR0zV0TfPuS4CJU5CEiZgTNE3D7zPw+0q3S6VSqqg2NDSkDh05Sk1t/Rjh5RRqX8mMjWXn8gHmjD+wxskLGe8nSmH+bCe65v2u9P7w0fP3NS1/nPzx/mMUnZu/Xzh3cHgVh1lxwA26RlGZ+q/Z0Z6hz20bet0RQlIvKmfx41rRaxS/r4Fy9tcUkbCdgyRMxBlD07R8TWL4wFKZDpYsrpnSayilcBxVaOrrb+Kzhwkox1W4SqGUwnXBVQrXzd9X3qrSrlIoV9HV00N5eUX+3IHHvXPzf1yKHleo/DmW7ebPpejcgcdV0TE3f45y+6/rnTOmvR9N6fc2UYbuhYyR/+Pd1r2fRvFxnWwmzVv73i+c339u4bahYWgaunHS8eGuXXys8DreY8WPj3R+f+CejiRMhJhGmqZhmhqmqRMKTN8/r717LZYsWTht15uo/sBR7uBAchXs3/8RCxYsGBxoxYE1KCTHCM5hrlH8uON6xxzXxXUUTj68+48NPD7wUylIZ+3C84vP7z+n+PxSKw4ZpVz8b/9iUPgMDaHBAVcchkOPDwRZVcXMblkhYSKEGJOmed/eGWYAXTigz+m9diaymKLK1+j6g8lx1EkhNDiwisOt+Hw3f/vkgBsUhq6iq7uHaFn5QMAptyjsvJ/ZnDMo+IZcy3ELoVrsvEVVpfh1jkjCRAgh8rRCH5HBTEzJ9YLurGm73kDguMRTuWm77nhImAghxGmiv+8GdLS0NbOvPaOvJoQQ4rQkYSKEEGLKJEyEEEJMmYSJEEKIKZMwEUIIMWUSJkIIIaZMwkQIIcSUSZgIIYSYMgkTIYQQU1byMNm2bRtbtmwB4I033mDdunWsW7eO//E//gfJZBKAeDzOn/7pn7JmzRpuuukm2tvbAcjlcmzevJk1a9Zw/fXXs2/fvlIXVwghxCSUNEzefPNNnn32WcALjC1btrB9+3ZeeOEFzjnnHLZv3w7Ao48+ysqVK3n55Zf57Gc/ywMPPADAU089RSgU4uWXX+auu+7izjvvLGVxhRBCTFLJwqSnp4ft27dz2223AXDgwAHmzZtXWL3zqquu4kc/+hEAr7/+OuvWrQPg2muv5cc//jGWZfH6669z3XXXAXDppZfS1dXFsWPHSlVkIYQQk1SyMNm6dSubNm2ivLwcgIULF3LixAnef/99AF5++WU6OjoAaGtro7a2FgDTNIlGo3R1dQ06DlBbW8uJEydKVWQhhBCTVJJVg7///e/T2NjIqlWreOaZZwAoLy9n27ZtfOUrX8F1XW644QZ8vuEXeVZKoes6SqlBu5L1H5+IXbt2Teo9xFMOB4+m6A4N/RXt3bt3UtecKXO5fHO5bDC3yydlm7y5XL5SlS2ectjp7/S2fZ4BJQmTl156ifb2dtavX09vby+pVIoHH3yQG264ge9///sAvPvuu7S0tABQV1dHR0cHDQ0N2LZNMpkkFotRX19PW1sb8+fPB6Cjo4O6uroJlWX58uUEAhPfuKezN03GOEF1RWjQ8YlstDMb5nL55nLZYG6XT8o2eXO5fKUsW0dvmkt+a2F+Sfrxy2azk/oSXpJmrieffJIXX3yRHTt28Jd/+ZdcffXVbNmyhVtvvZXW1laUUvzTP/0T/+W//BcAVq9ezXPPPQd4QbRy5Up8Ph+rV69mx44dALz99tsEAgHmzZtXiiILIYSYgknXTLq6uqiqGv+2kLquc9999/GFL3yBXC7HqlWr+PznPw/A7bffzpYtW1i7di1lZWU88sgjANxyyy1s3bqVtWvX4vf7+eu//uvJFndSHEdh2Q4+05jR1xVCiFPNqGFy66238o//+I8APP7443zxi18sPPb5z3++MOx3NBs2bGDDhg0AXHnllVx55ZVDzonFYnznO98ZcjwQCLBt27YxX6MUIiEfi+aV096ToTeZRsPb0jNruUP6coQQ4kw3aph0dXUVbr/yyiuDwkQpNdxTThtBv8n5i2sAyFoOiVSOnkSWjrbDdPdlUAoUEPAZhPwmpimLCQghzlyjhsnJI6lGeux0F/AZBCpCVFeE6DkRZsXFC0mmLfqSWTp7M7T3pMkmHDTN24M5FDAJ+Iwz6nckhDizjRomxQEiH4wDDF2jPOKnPOKnqa4MgEzWpi9l0d2XoaMnTXdfFlcpNCDgNwn5DQxDai9CiJkx061H466ZiNEFAybBgEltZYil8yuxHZdk2qI3kaOzN0VHbwbbdlEKfKZOMGDiN3X5HQshJs1RLpZjYTkWOccibWVI21kyVoZkSuGqBejMzACiUcNk//79hWVODh06VLgNcPjw4dKW7BRnGjoV0QAV0QDzG8pQSpHO2iRSFp3xDB09KbriGe9kDYI+k1DAnPCYcCHE6c1RLrZjkXMtLMcmY2VI2RkyVhbLtQadq2sGpm7gM31YbnJGyzlqmPz93//9TJXjtKdpGuGgj3DQR11VGKjCsh0SKYveZI727jRd8TSuq1AK/D6doN/E75NhyUKc7lzlYjk2luvVMtJ2lrSVIWNnyTk5b7RP/numrumYuolpmATMiU/ILpVRw+Q9nqTQAAAgAElEQVSyyy4bcqynp4eKigppnpkGPtOgstygsjzIwsZyXFeRytokUjmvY787TTyVH5asa4T8Xse+1F6EOPW4ysV2bHKuFxpZK8uJbAe5Npeskxt0rq7pGLqBqZlEfJFxf95qrkMg00so3UmfFSzF2xjRqGGSSCT46le/yg033MBll13Gf//v/52XX36ZpqYm/uEf/oEFCxbMVDnPCLquEQ35iIZ8NFRHgIFhyfFEjraelNex73od+36fQdBv4pNhyULMCUopLNf2+jFcm0x/DcPKkHGyg87VNJ2sm0PX9AkFBoBhZwilugilu/I/OwmmughmetCVC0AothTYMJ1vb1Sjhsm2bduIRCIsWbKEN954gzfffJNXX32VPXv2sG3bNv7u7/5upsp5xioelryoqQLHVaQyFvH8sOSO7jS9SQcAQ9PI2TKpUohSUkphuzaWY5NzLbJ2rtCPkXVyqPyHOXiBYWoGhm4MGxh9uomhj/AxrBSBbJxQqotgunNQePitgf4QV9PJBCtJh6vpqjmbdKiKTLia/bkgHy/Jb2B4o4bJr371K55//nk0TePHP/4xv//7v09jYyONjY2zNjP9TGfoGmVhP2VhP021A8OSE2mL7r4s7a2HBzr20Qj4vJFjpgxLFmJC+msYtmOTdXKkrDRpO0vWzuIWBQZomLqJoemEzdCEv8jpjkWwUMMoqmmkuzFcu3CebQZJh6roqVpEOlRFOlxNOlRFNliB0of2rbrd8cm+9UkZNUwMY2Di3S9/+UtuvfXWwmOn+wz4U0n/sOSaWIh4W5gVKxaQSFv0pXK096Tp7Elj2S4Kb5RZyG/gl0mVQhRqGJZrkbUt0na60PE9ODDIB4ZByAyiaRP8cqYUPis5JDAu7Gsn8uFALUMB2WAF6VA1vbEFRaFRie0Lwxz+NztqmOi6Tl9fH6lUig8++IDLL78cgNbW1hH3IhGzzygaltxcVzQsOW3RHfc69jvjmf7BIQT9JsGAgTHBvWKEOBW4KNJ2xgsN2yJtZ0hZaTJ2Fsd1CqOkQMPIj5QKmgF0beIjKTXXIZjpIZTqHFLbMIv6TBzdRzpcRVeols6qFaRD1WTCVWSCMVxj6p+t6qQgnAmjhsnNN9/M9ddfj1KKNWvWUFtby6uvvso3vvENbr755pkqo5iiQcOSK8MsWwCW7ZJI5+hL5msvvRlsx/sf0GfohAIyLFmcOgpzMfon7+Un7qXtDMeSx0m25/C+94OhGZi6ScD0TyowAAwrQ+ikfgyvaaoHjYFWm6w/SjpcRUfduaRD1aTDVaTDVeT8ZaBpnDhxjIaGyW+roZSL7TrYro2jnMJxTdOJ+qNozFxNZtQw2bBhA0uWLKGjo4MrrrgCgO7ubr7whS9w/fXXz0gBRWn4TJ3KsiCVZUHmN3jDktNZm75Ujq78emOdvWlveLumEfJ7I8dkWLKYLW7/bG/XJudY+ZFSXj+G5Ywwec/wETYDRP2Rib+gcgsd4F5gdOY7w7vwW6mBcmkGmVAlqXAtnTXLvMAIVZMJVeJM0zyQ0UIjaAaoDFQQ9oUImH78ug/TMOmKZ2e0KXvM/UwuvPDCQfc//elPl6wwYvboukYk5CNSNCw5Zzkk0ha9fVnae9J09WVwXe9bl980CAVkWLIoraydJZFL05PpIZ5N0l+7QHnN8Iaer2UYk//Q1p0coXT3oLAIpToJZXrQizrALTNEOlxFd9XigcAIV5EJVsBE+1BGMJnQ0Kfptadq1DApXj5lOC+88MK0FkbMLX6fQZXPoKo8yKKmClxXkcxYJFIW7T0pOnoyxJNZFN6wZH9+QqWuebUZTSu+PdvvRpwKHOWSttLEswm6073e7G/AZ/iI+MKT/6atFL5c8qSmqU5C6S4C2b6B09DIBCtIh6vprVyUDw2vacr2hafjLeaL44VG1rVI5BKF46dCaIxk1DBJpVJks1muu+46Pv7xj2MY0oZ+JtOLhiU31ni1l0zOJpm26OnL0pPI4jgKx3GxXRfLdnFd5R1zXeIph854GiYwEFABuqYVVgEYLqhGCi8ZrXZqyNk5knaannQvvdm+/DwpnYDhJ+qPTuhammsTTPcM6sdY1ttK+f4+zKJZ5o7hIx2qJl7RUggLr2kqhhpp3sckjFXTiBph5lc0nVKhMZJRf2v//u//zttvv82zzz7Lvffey9VXX13oRxEC8iPB/CbVFaExz/25v5OLL16A6yrvj1K4isJ9pbxjTtHjSoFte+Hk2ArbcXFcF8fFu+14gdUfXt59cFy3eDmjk2icnGgKiKdsOnrTXnhpFH56IVV8e+gxCa/xc5VLxsoSz3m1j7TtjSw0dV9+nsbYH6amlS7ULApNU+muoR3ggTJ6jAjt1ctJhyoLTVM5f3Rah9n2T2Q8OTTQNEJmcMSaxt4eRXW4ctrKMZvGjOCVK1eycuVKMpkMP/zhD3nooYdIJBKsX7+eG2+8cSbKKE4TuqZ5kydnqILr9AdUcXi5RQGmvMf7w8vMtXHekhocRw0KL9v1Aqo/vGxX4bou2Xx4Oa53DRgtvKA4wNSwR0d+Rjzt0NmbHvtNj3bRcZ1U/A7UiIeKL9E3jrLZrk3aztKXSRLP9eXncGj4dT8+I4gCLMDCBQaGtSogmItT2/sR5ek2IpkuItlu/PbA6zmaQSpQSW+whmMVS0kGqkgGK0kFYjhGgPb2Nmpr6/LXU2iWBjlvSLBS+UzR8m8pXwvuv9+fN1r+uELh5APDUU7+SwSF0KgIlBP2hQj6/PgNP75TuKYxUeOuzwWDQdasWUM4HObJJ59k+/btEiZiTjP0/KfEOMPraJlJc36zs4lSRbWsQoDlfzr5laBddXKoqcLQzeG+JPfXdDQgYLdxwbn1A4+d9Lyxnl/0xEHPH/O5RY8NTMcYfN1f6R1cvKJ5yDVSuRQ9mTjHk+30ZfoIK0XM8BH2V2JoxkmvW/R6SmF2HsN/ZA+BI3swu1sBcANh7IoanMZz6SuvxiqvximvwQ5VoHQdpRQBBX4FlXh/J0pBwID5C8KgBo71b7utVP/fF94f7yCOq8jZ3qgx23EKtWilNIK+ACEzStAMYup+b7kUzUAprXAtK6vIKgeUF1ra0MowgNf0O1IQFz1HDT2ERr5GzOB+ycLtGZ5XPq4w+dWvfsVzzz3HD3/4Q84//3z++I//mE984hOlLpsQpwxN0zC0/gCbfrGISV3l9HUAT6egTycc9GE7Nr3ZPjqSXRxPtJFzcmhoBM0gtWWx0ZsBbQvf8Y/wHf4A/+E96OkEStOw6+aTWvn75FqW4lbUDHmawdjfFcIhqCgb/qNOKZWfm5LDzo/c6h8OX+aPUBGoozxYRsgXJGQG83NTxl/TcF01KLT6v1SofLD9UuvgoouaUP3n9gceDHt+/88htez8H0e5qHwzr888ObBLa9Qw+du//Vuef/55wuEwn/rUp9ixYwc1NUP/QoUQZ6ZULk1ntoedx35NV6obVyl8uknYHxpzboeWSuA7sgf/4T34ju1Dc2yUz0+uaQlWy1KsprNRwekJ0OFDw+voL/NHaAjVTik0RjIwL2v4T/WgXyca9k/5deaCMcNk3rx5NDQ08NOf/pSf/vSngx7/zne+M+YLbNu2je7ubh5++GHee+89tm7dimVZNDY28vWvf53y8nLi8Thf+tKXOHz4MFVVVTz66KPU1taSy+W4++672bVrF8FgkEceeYTFixdP7R0LISbNcR3i2QSdqW6OJ9pIW2kOpo8TzEWJBcfY50gpjJ42fIe82ofZcdS7ZqSC7NkXk5u/DLt+IUxh1OhwodFnpejJxksaGmKMMHnooYemdPE333yTZ599liuvvBKABx54gL/8y79k9erVPPzww/zDP/wDmzZt4tFHH2XlypU88cQTPPfcczzwwAM8+uijPPXUU4RCIV5++WV+/vOfc+edd/L0009PqUxCiInJWBl6s32c6GunPdWJq1x0zSDsC1EVqqTb10nYN8JoPsfBbD2I//AH+A7vwUj0AGDXNJG6+CqslmU4lXUTHlk1rppGoIyQP0ik28fHzrpcQqPERg2T0ZZM+clPfjLqhXt6eti+fTu33XYb77//PgCu65JMeitkptNpKioqAHj99df553/+ZwCuvfZa7rvvPizL4vXXX+f2228H4NJLL6Wrq4tjx44xb97k17IRQozOVS6JbJLOdA/H+9royyW9zdgMP+WBsjE/lLVsGt+RD/Ed3oPv6F50K4syTKx5Z5G58OPkms9Ghcc/0EEpRcbOkrG9rRVGCo2RahoBXWofM2HUMHnvvff42te+RiwW48EHH6Sqqopjx47x4IMP8uMf/5h33313xOdu3bqVTZs2cfz48cKxLVu2cOutt/Lggw8SCoUKtYy2tjZqa2u9Apkm0WiUrq6uQccBamtrOXHixITCZNeuXeM+d7x27tw57decTnO5fHO5bDC3y1fKslmuTcpJ02P10WPFcZRC0yCo+/GNYxLfkV2/INZ5lFjXEcp629FQ5HxBOqub6a5qpi/WgGvkr3O8FWgd9XpKKbJuDkvZoCBihqnyVRAw/Ph1P37Nh6YpUsRJMfa+HWfq3+tMGvX/kq9+9ausWbOGY8eO8e1vf5vLL7+cLVu2cNFFF7Fjx44Rn/f973+fxsZGVq1axTPPPANAJpPh7rvv5p/+6Z+48MILefLJJ/mf//N/8sQTTwx5vlIKPT/Ur7gNtv/4RCxfvpxAYHoWWwPvL/6SSy6ZtutNt7lcvrlcNpjb5ZvusimlSOZSdKd7OZZooy+TxFUalUY1Tb5mjGE2WxrEdTHbj+A7/AHs20Uo7X2g25X1ZC78XXIty3Bq5mFoGjXAeIbtOK5DykpjORaaplETrqKxrJ7KUAUBc/Kd1GfS3+t0yGazk/oSPmqY9PX1ceutt+I4Dtdccw0vv/wy9957L2vXrh31oi+99BLt7e2sX7+e3t5eUqkUR48eJRAIFBaO/MM//EO++c1vAlBXV0dHRwcNDQ3Ytk0ymSQWi1FfX09bWxvz588HoKOjg7q6ugm/SSEEg4fu9rWRcweG7o7ZeQ5gZfEd3eeNvjqyBz2bRuk68fI63At/B6tlKW40NrEyuTbJXBrbtTF0g4ZoLQ3RWiqCZfimYV8PMXNGDZNQyOtUMwyDbDbLE088wXnnnTfmRZ988snC7WeeeYa33nqLO++8kzVr1rB//37OOuss/v3f/50LLrgAgNWrV/Pcc89x22238dJLL7Fy5Up8Ph+rV69mx44drFy5krfffptAICD9JUJMQCqXpjvTy4lE+9Chu/rYy7LriV58+c5z34kDaK6DGwhhNZ9NrmUp1rwl7Dt8mMWLx7/EUs6xSFlpXOXg1/00lzdQF62hPBAdu0Yk5qxRw6R4a97KyspxBclIKioqeOihh7jjjjtQSlFdXc2DDz4IwO23386WLVtYu3YtZWVlPPLIIwDccsstbN26lbVr1+L3+/nrv/7rSb++EGeC4YbuAuOvfSiF0XnMq30c+qAw+9wpryZz7mVYLcuw61pggs3N3t4jGVzlEvaHOKtyPjXhSqKBiHSOnyZGDRPXdent7S2ESvFtgFhs7Crthg0b2LBhA+DVQFavXj3knFgsNuyclUAgwLZt28Z8DSHOZCcP3XVcF0MfGLo7phFnn7eMOvt8NEp5W+Vm7AwKjQp/hHNqFlMZrpjaUvJizho1TPbs2cPHPvaxQoD07wEP3nIDu3fvLm3phBBDDB6620pfLjWhobsAWjqB7/D0zj53lUvKSpOzc4BGVTjGkqqFVIUqCPqCk3uz4pQxapj0zw8RQswuy7VpT3bSmuigLdGBrRw0TSNshqgOjaPTu3/2+eE9+A99gNFxFI2pzz53XIeUk6Ez1Y2h69SGq2ksrycWLMcvHehnlOnbBUYIMW1OHrq7K76H3mMZ/IaPiD88vo7qUWafp6cw+9xybJJWClc5mJpJzFfOyqYLqQiWY0oH+hlLwkSIOWKkobshX4ioGR7XJkqjzz7/XXLNSyc0+7xf1s6RttI4yiVoBlgQa6I2XEV5oIxf9vzytNngSUyehIkQsyiVS9OV6aE10THq0N3ROqz1eOfA6Ku2Q2hK4Yai5BaehzV/GVbjWWBOrMmpeAkThSLii7CkehHV4RhRf0Q60MUQEiZCzABXuflv9xmSVoreTJyudG9hvalxD92FQbPP/Yf3YPR2APnZ5xcMzD6fzOKJKStN1smC0qgIlbOosoWqUIywf+xtmcWZTcJEiGnUv5pt2s6QyqWJZ/vozcSJ55KQ39xI0zR8hknA8BMez9BdQLctfAd+k599/iF6NoXSdeyGhWTOuXRSs8/BC7lkLoXlWmh4S5g0lJ1FZaiCoDl9yxCJ05+EiRCTZDkWaTtL2koTzyaJZ+L0ZvuwXYf87rP4DBO/4aNinEN2i+nJXq/v49AHXHz8I3TlerPPm84mN9+bfY5/4h/4tuuQzKVwXBtdN6iP1tAYrZMlTMSUSJgIMQbHdbwJeFaWRC5FT6aXeLaPjJUrbKBnaDp+w0/EN86RVsNRCqPzeGH0ldl1wnv98mpa5y0jcsHlk5p9DsVLmLj4dJPm8gZqo9VUBMpkCRMxLSRMhMhTSpG2MoP6NXozfSTzS5IAaGgETD9+w0/YNw1byhbPPj/yIXqqb9jZ50f27WVxw4IJXTprZ0lZaZSCoC/AWbEWqiOVlAWisoSJmHYSJuKMM1K/xq74B3QfTA3q1/AbfirH2zE+vhfH6GnHbDvsDeE9vh/NtqZl7/OBJUyyKKUoD0RZWnMW1eFKWcJElJyEiTitTaRfI2IEqRzPbPIJ0HIZjPajmO2H8bUdwWg/gm5lgfzs8yUryLUsw25YAMbE/zl6S5hkyDk5NKAyWMHiqgVUhWKEZAkTMYMkTMRpYTr6Nab8zV0p9HgnZtsRzPbDmG1HMHravNDSNJxYHbmzlmPXtmDXNeOWVU14+G7/e01ZaXKOha5r1IarmVeWX8JkCptICTEVEiYj6MsmePfE++i6hl/3EzB8+E0/bdlOjvW1YuompmZg6gZG/o+pmxiaLs0JJTTcfI2S92uMxMphdhzFbDuM2X4Es/0IetYrh+sPYtc2k1t0nhceNU2TGnnVz1EOPZk4rnIwNJPGaC11ZTXEAuWYk6jRCDHd5P/CEViuTSKXJOqPkHLS9OWSuMrlWKYNo/WDwnla4b8DS/P7dC94+ucS+A1f/qcf08iHjm4MCSPpFB0wkfka096vMXyB0BM9+eDI1zq6W9HyK2o7FTXk5p+DXduMXdfiLdk+xfJk7RxpO43rKnKuzfyKedRFqr2VgScxokuIUpIwGYWuaUP2no6aYapGaVdXSuEqFyf/DTqVSxfuO65T+MArjp/+26Zu4Dd8+AcFUH8w+QaFT3EgneofLKWerzEptoXZeXyg1tF2GD2TBECZfuzaZjIXftyrddQ2oQJTnyGulCLr5EhZaUAR9oVZXLWQ6nAle3rDLK05a8qvIUSpSJhMM03TMDQDg4mN3e8PIVe5WI5Fxs7iuC6ucnCUixc3Gtqg5wCawtAM/IZXEzqcPEygNUrADBAwfF4IFQXQybWhmeQql0QuWfr5GpOgJ3upaj9AuGMvZvsRjK7jaK4LgFNWhdW0pFDrcGK1k5rrMZzCCCwrCxpUBMs4N7aE6lDloCVMpOlUzHUSJnPEZEMIKISQ47qk3QwdqS5c16sNKdxCBKl8Hcj7r0JDx2/6vNpPvl/IZ/q9GpHZXxMyMXR9oCY0jn6hkfo1dsffp/tQGpRC03Sv9mWWuF9jOI6N0XnCa65qP4Kv7TB6qo8YoAwTu6aJzPmrsOtasGubUcGx90qfCNt1SFtpLNdGA6rDlZxdvUiWMBGnNAmT04Cu6eiajqlDQPcT9Y/vw08pVWh+K+4X6v8DcHJkeH0VXr+Qrz+I8k1yCoiP0q8RNUZvIiwVLdVXaKoy249gdhxDcx0AnGgMq34Bdl0zB7LQeOFKmOYaUfH8D1D4dB/1kRpvBrpsIiVOExImZzBN0wrNXhMxUr8QMGq/xow01bgORlfroPDo3xRK6QZ2zTwy5142UOso2tsjtW/vtAVJYfkS10XToSpUyVmV84kFy4n4ZQKhOP1ImIgJm0qT3LSXJZPKj67yRliZncfQbAsAN1yGXdvihUdtC051w6QmBo6HN/cjg+XmUGhEfWEWxpqpDnvLl8gOhOJ0NyNhsm3bNrq7u/nc5z7Hli1bCse7urqoqKjgxRdf5NixY2zevJnOzk4WLVrEI488QiQSIR6P86UvfYnDhw9TVVXFo48+Sm1t7UwUW8w1rustRZIfmmu2H8aIdwGgNB2nuoHs2b+FXdeMXduCGymf8vDckQzaPEqBaRjURaqpi9RQHiyTvg9xxil5mLz55ps8++yzXHnllZx77rns2LEDgHQ6zWc/+1m++tWvAnDvvfdy4403snbtWr71rW/xd3/3d2zevJlHH32UlStX8sQTT/Dcc8/xwAMP8Oijj5a62GIO0LJpzPxSJGbbEcyOI2hWDgA3GMGubR4Ij+p5E95NcKJyjlXYurZ/6ZKFsWZiIa/pSuYJiTNZScOkp6eH7du3c9ttt/H+++8Peuzxxx/n0ksvZeXKlViWxc9//nO+9a1vAbBhwwZuvvlmNm/ezOuvv84///M/A3Dttddy3333YVkWPp90Wp5WlELv7Rg0m9zsafce0jScynqyiy/Mz+toxi2rLFmto5+3REuWnO0FWMgfZH6siepQJeWBqMw8F6JISf81bN26lU2bNnH8+PFBx/v6+nj66ad54YUXAOju7iYajWKaXnFqa2tpbW0FoK2trdCsZZom0WiUrq4u6uvrS1l0UWpWdnCto/0Ies7bwtYNhLBrm0ktWu51lNc0ga/0a04Vmq6sDC7e/J3aSBV1kRoqgmWycKIQoyhZmHz/+9+nsbGRVatW8cwzzwx67Pnnn+cTn/gE1dXVgPeP+OTRLSONdlFKTWjG965duyZYck+fneRg4iBdvo4hj+3bt3dS15wpc658ShHIJKiOt2N/+BbRvnZCyV40FApIhyvoqWomWVZDoryWTKhsoNaRcuDQoZIVzVEOWTeHk5+gePjAQWK+cqJmmKDux+pJcZRDHC1ZCcZv586ds12EEc3lssHcLt9cLttElCxMXnrpJdrb21m/fj29vb2kUikefPBB7rrrLn70ox/xxS9+sXBuVVUVfX19OI6DYRi0t7dTV1cHQF1dHR0dHTQ0NGDbNslkklhs/HMVli9fTiAw8c7QrnQP1lGGLEm+b99eFi9eMuHrzZQ5UT7byi+AOLB6rp5NAeD6Aji1TWSWXOT1ddQ0owJBTKAi/6eUXOWStjJkHa/pKmD4aYjWUh2pZN97H3L5pZeXuASTs3PnTi655JLZLsaw5nLZYG6Xby6WLZvNTupLeMnC5MknnyzcfuaZZ3jrrbe46667UErx3nvvcfHFFxce9/l8rFy5kpdeeol169bx3HPPccUVVwCwevVqnnvuOW677TZeeuklVq5cKf0lc4yW6sN34mBhiK7R1YqWn3filFdjtSzFrm3mo6xi3vLfmralSMbDWzAyR8rOoJSLrhnUhCupj9RQESwn5AsWasEHdekDEWKyZvxfT1dXFz6fb0ht4a/+6q/YsmUL3/72t2lsbOR//a//BcDtt9/Oli1bWLt2LWVlZTzyyCMzXWRxEi0Zx3fiAGbrQXwnDgwMzzV93lIkF/x2oaO8eMfA9L69MxIktmuTsjLYrg0oygNlLKlaSGWogjJ/RPY8F6IEZiRMNmzYwIYNGwCorq7mJz/5yZBzmpqaeOqpp4Ycj8VifOc73yl5GcXI9GQv5okDmCfy4dHXDXhNVnb9ArJLL8FqWIhT1TCjtY5+rnLJ2FmydhalvFn4DWW11IWrKQ9EZcMoIWaA1OvFEHqiB/PEAa/p6sSBwnIkrj+IXb+AzDmXYjcsxKmsn5XwgP69PjK4ykHXdKpCMc6qyi9XIvudCzHjJEzOdP2bPuVrHeaJAxjJXiA/RLd+AZnzLseuX4hTWTdr4VG80i5A1BfmrFgLVeEYUVmuRIhZJ2FyplEKva+70N9hnjg4ODwaFpJZvmogPGbpG37xSrtKKfyGj7r8SrvlgagsVyLEHCNhcrpTCr2vq9BkZZ44iJGKA+AGw9j1C8ks/22v2SpWO2vhAUUr7fYvVxKKsaiyhcpgBWF/SJYrEWIOkzA53eSXJekPD1/rQfRUH+CtZ2U1LCTTsACrYeG07FM+FYNW2lUQ8YdYGGumKhyj3C/LlQhxKpF/rae6k8LjoqP78Fv5ZUlCUayGhdgNC7DqF+JWVM9qeCilSFsZb6VdwNQMaiPV1EWrqQiUEZTlSoQ4ZUmYnGqU8pZhbz1YGHGlZ5KAt39Hd6yB4JLlXs2jvGpWwwPAcqzCnI+EnSZg+lkYa6YiVE5UVtoV4rQhYTLXKYXR0zYw2qr1IHrGW5rECZdjzVvs1TwaFuKWVbJ//75ZXU7FVS4pK0POyQIQNIO0VDRSHa4k1hvisuYVs1Y2IUTpSJjMNUphdLcODo9sGgAnUoHVdDZWwwLshoW40dis1zz6JXJJsraFoevURaqpjVRTESwj7AsVzjmgyfBdIU5XEiazTSlvz/ITB7zwaDs0EB7RGFbLMqz6fHiUjX+By5mStbP05ZLUhCtZ0bCQ8kDZhFZ1FkKcHiRMZprrYnS3FuZ4mK0HC/t4OGWVWPOXYdV7neZudO6FRz/btenN9hEyg1zSeAE1kSqZdS7EGUzCpNRcF6PrxODwsLz+BKesityCc7HzI67cSKkXYJ86V7nEM32gaZxbs4Sm8gZZOFEIIWEy7VwXo/P4wKq6rQcL+5Y75dXkFp2P3bAQq34BKlI+y4UdP6UUiVySnGOxMNbMwsoWArKAohAiT8JkqlwHo6M4PA6h5fcMdypqyJ51AXb9QqyGBahw2SwXdibCj9YAABfhSURBVHLSVoaklaIuUs3S6rOIBiKzXSQhxBwjYTJBmutgth0eWFW37RCabQFgx2rJLr5woOYRjs5yaafGcizi2QQRf5hLmy6iKhSTfhEhxLAkTMbi2JgdxwrhcXHrQQzXAcCO1ZFdssKbZV6/ABU6Pb6xu8qlNxvH0AzOr1/KvGi9jNASQoxKwmQYSimy775O47uvEeo8geZ4y57blfV0NCwhvOwiLzyKdhE8HfT3i1iOzaKqFhbEmvEbskWyEGJsEibDUmR/8SMM1y7sImjXz0cFwxzat5fFC2ZvhnmppKw0aStDQ1ktZ1ctIuwPjf0kIYTIkzAZhqbplP/J/Xx49F0qQ3N3rsd0yDkWfdk+ygNlXNC8gsrQ3B+eLISYeyRMzlCO6xDP9uEzTC5oOJeGaK0suiiEmDQJkzOMUop4LoHrOiyuXEBLbB4+6RcRQkzRjHwV3bZtG1u2bAFg//793HLLLVx33XV8/vOfp7fX2zL22LFj3HTTTXzyk5/kz/7sz0gmvWXV4/E4f/qnf8qaNWu46aabaG9vn4kin5b+//buPajqOv/j+BM5R5CLEclBfnhZLW8/KVEpJQxHdgTxgBjZZiFu2ma629LSaKIZDDqaIqOGF6xtc3fTRtNEzIi1bM1dxdXFTGlQS/NCEoIgd8718/uD8YwX8CeXIwd7P2aa5Mv3fL+v8wZ9c86X7+dda6yjvL4CX/eHGNP3Cfo/1FcaiRCiXdi9meTl5ZGVlQU0/lQ8Z84cXn75ZXbv3s2QIUN47733AEhNTeWFF14gNzeXgIAANmzYAMCaNWsICgri888/59lnn2Xp0qX2jnzfMZiNXK2roJvGhdG9RxDgO5huMohKCNGO7NpMrl27xurVq5k9ezYA3333HW5uboSGhgIwe/Zs4uLiMJlMHD16lIiICABiY2PJzc0FYP/+/URHRwMQFRXFgQMHMJlM9ox93zBbLZTXXcOizAT2/F+Ceg3jAdfOs4SLEKLzsOs1k+TkZBITEykuLgbg4sWL9OjRg4ULF1JYWEj//v156623qKiowMPDA42mMY6Pjw8lJSUAXLlyBR8fn8awGg0eHh6Ul5fj6+trz+idmlKKKkM1SikG9uhPrwf80MhijEIIO7JbM9m+fTt+fn4EBwezc+dOAMxmM0eOHGHz5s08+uijrFmzhuXLl5OYmHjbMh3NLduhlGrR3dgFBQWtyl9truVCzQXKtWW3fe7s2R9adcx7od7SwPHTJ/Dp6o2v60NcrSzhKiUdHcsmPz+/oyPckSPnk2yt58j5HDlbS9itmeTk5FBaWkpMTAyVlZXU1dVx6tQp+vbty6OPPgo0vm2VkJCAt7c31dXVWCwWnJ2dKS0tRafTAaDT6SgrK6Nnz56YzWZqa2vx8rr7ez8CAgJwcXFpcf7y+muYfuK2+0zOnv2hQ8fiNqfBbKDGUIsqLif2yXC6uzjeumD5+fmMHDmyo2M0y5HzSbbWc+R8jpjNYDC06odwu10z2bRpE3v27CE7O5uEhATCwsLYunUr5eXlnDp1CoCvvvqKoUOHotVqCQoKIicnB4Bdu3bZrquMHTuWXbt2AY0NKigoCK1WfgPpOrPVTHldBQpFkP9jPOLexyEbiRDi/nZP7zNxdXVl/fr1LFq0iPr6enr27ElaWhoAKSkpJCUlkZmZiZ+fH6tWrQLgtddeIykpCb1ej6enJ+np6fcyssOyKiuVDdV0cXJiiM8A/qe7L85dnLng9GNHRxNC/ALdk2YSGxtLbGwsAMOGDWPHjh237ePv78+HH35423YvLy82btxo94ydhVKKamMtJquZX3n1op9XL7rKkCohRAeTO+A7kXpTA7XGOnw9ejCgRz88ut4fS94LITo/aSadwPUhVR4u7jzRKxBvt/t78UkhROcjzcSBXV+MUdNFQ4DvIPw8dDKkSgjhkKSZOKDr10XMFjP9vfvQx8tfhlQJIRyaNBMHU2eqp85Uj5+nToZUCSE6DWkmDsJoMVFlqMbLtTuP+Q7GS4ZUCSE6EWkmHcxitVBpqKars4ZhPf8XX48eMqRKCNHpSDPpINcXY7QqK4889Cv6dP8fNM7y5RBCdE7yr1cHqDHWYTAb6NW9J/29+8psESFEpyfN5B4ymI3UGGt4sJsXI/yG0t3Vs6MjCSFEu5Bmcg+YrRaqDFW4alwI9AtA5/5Qs0vsCyFEZyTNxI6sykqVoRqAQT0exr+7DKkSQtyfpJnYSbWhBqPFRB8vf/o92BtXTctnqgghRGchzaSdNZgN1Bhr8XHzZmCP/njKbBEhxC+ANJN2YrKYqTJU4961G0H+j/FQtwfluogQ4hdDmkkb2YZUdXFiqG4gfp46nOW6iBDiF0aaSSsppag21GBSFhlSJYT4xZNm0gr1pgZqjHX4efow4KF+uHd16+hIQgjRoaSZtIDRYqLSVEPvLl0Y1TsQ724ypEoIIUCayV1pXIyxCq2zll9182d07xGyGKMQQtxAmskdKKDKUI3ZauHhB/vSx8ufE+UnpJEIIcQt7kkzWbFiBRUVFSxfvpx169bxySef0L17dwB+85vfEBcXR2FhIW+++Sa1tbUEBQWRmpqKRqPh8uXLzJs3j6tXr9KvXz/S09Nxd3e/F7GxWC34eeh4+KG+uGllSJUQQjTH7j9i5+XlkZWVZfu4oKCAVatWkZ2dTXZ2NnFxcQDMmzeP5ORk/vGPf6CU4uOPPwYgNTWVF154gdzcXAICAtiwYYO9IwPQ3cWTkL6P82jPwdJIhBDi/2HXZnLt2jVWr17N7NmzbdsKCgp49913iY6OZvHixRgMBn766ScaGhoIDAwEIDY2ltzcXEwmE0ePHiUiIuKm7feCposz3eXudSGEuCt2bSbJyckkJiba3tKqra1lyJAhzJs3j6ysLKqqqtiwYQNXrlzBx8fH9jgfHx9KSkqoqKjAw8MDjUZz03YhhBCOxW7XTLZv346fnx/BwcHs3LkTAHd3d/785z/b9pk5cyYLFy4kNDT0pqVHlFI4OTnZ/n+jli5RUlBQ0IZn0bT8/Px2P2Z7cuR8jpwNHDufZGs9R87nyNlawm7NJCcnh9LSUmJiYqisrKSuro4FCxYwcuRIpkyZAjQ2DY1GQ8+ePSktLbU9tqysDJ1Oh7e3N9XV1VgsFpydnSktLUWn07UoR0BAAC4u7bdib35+PiNHjmy347U3R87nyNnAsfNJttZz5HyOmM1gMLTqh3C7NZNNmzbZ/rxz506OHDnCvHnziIyMZNSoUfTq1YstW7Ywfvx4/P39cXFxsRU2Ozub0NBQtFotQUFB5OTkEB0dza5duwgNDbVXZCGEEK10T+8z8fb2ZvHixcyZMweTycSIESOYMWMGAOnp6SxatIiamhqGDh3K9OnTAUhJSSEpKYnMzEz8/PxYtWrVvYwshBDiLtyTZhIbG0tsbCwAERERtt/OutHgwYPZsWPHbdv9/f358MMP7Z5RCCFE6923d8ArpQAwGo3tfmyDwdDux2xPjpzPkbOBY+eTbK3nyPkcLdv1fzOv/xt6t5xUSx/RSVRXV3PmzJmOjiGEEJ3SwIED8fT0vOv979tmYrVaqa2tRavVysRDIYS4S0opTCYT7u7udOly97ci3rfNRAghxL0jy98KIYRoM2kmQggh2kyaiRBCiDaTZiKEEKLNpJkIIYRoM2kmQggh2kyaiRBCiDaTZtKE+Ph49Ho9MTExxMTE8O233/Lpp58yceJEwsPD2bJli23fQ4cOER0dTXh4OKtXr7ZbppqaGqKioigqKrrjeQsLC4mNjSUiIoI333wTs9kMwOXLl4mLi2PChAnMmTOH2tpau2VbsGAB4eHhtvp98cUXrcrcHtatW4der0ev15OWltaqHPaqXVPZHKl277zzDhMnTkSv19tWAXeU2jWVzZFqB7BixQqSkpLueK7m6lNVVcWsWbOIjIwkLi7uphEdDkuJm1itVjVmzBhlMpls237++Wc1btw4VVFRoWpra1V0dLT6/vvvVX19vRo7dqy6ePGiMplMaubMmWr//v3tnun48eMqKipKDR06VF26dOmO59Xr9eqbb75RSim1YMECtWXLFqWUUrNmzVJ79uxRSim1bt06lZaWZpdsSikVFRWlSkpKbtqvNZnb6uDBg+q5555TBoNBGY1GNX36dPXpp586RO2ayrZ3716Hqd1//vMfNXXqVGUymVR9fb0aN26cKiwsdIjaNZXt7NmzDlM7pZQ6dOiQGjVqlJo/f/4dz9VcfVJTU9W7776rlFIqKytLvfbaa+2WzV7klcktzp07BzROgZw0aRKbN2/m0KFDjB49Gi8vL9zc3IiIiCA3N5cTJ07Qt29fevfujUajITo62i4z6j/++GNSUlJsg8GaO+9PP/1EQ0MDgYGBQONqzbm5uZhMJo4ePWpbrfn6dntkq6+v5/LlyyxcuJDo6GgyMjKwWq0tztwefHx8SEpKomvXrmi1Wh5++GHOnz/vELVrKtvly5cdpnZPPPEEf//739FoNFy9ehWLxUJVVZVD1K6pbK6urg5Tu2vXrrF69Wpmz54N0Kr67N+/n+joaACioqI4cOAAJpOpXfLZy327anBrVVVVERwczFtvvYXJZGL69OlERkbeNKNep9Nx4sSJ22bX63Q6u8yoX7p06U0fN3feW7f7+PhQUlJCRUUFHh4eaDSam7bbI1tZWRmjR48mJSUFT09PXnnlFXbs2IGbm1uLMreHAQMG2P58/vx5Pv/8c6ZNm+YQtWsq25YtWzhy5IhD1A5Aq9WSkZHBBx98wIQJExzq++7WbGaz2WG+75KTk0lMTKS4uBi4/e/r3dTnxsdoNBo8PDwoLy/H19e3XTLag7wyucXw4cNJS0vD09MTb29vpkyZQkZGRpMz6q1Wa5Pb7a258za3valc9srZu3dv1q9fj06no1u3bsTHx/P111+3OHN7+v7775k5cyZvvPEGvXv3dqja3Zitf//+Dle7hIQE8vLyKC4u5vz58w5Vuxuz5eXlOUTttm/fjp+fH8HBwbZt7VEfpVSLFl3sCPLK5Bb//e9/MZlMtm8GpRT+/v43XQC7Pov+1tn1rZlR3xrNnffW7WVlZeh0Ory9vamursZiseDs7GzXnKdPn+b8+fO2l+5KKTQaTYszt5f8/HwSEhJYuHAher2eI0eOOEztbs3mSLU7e/YsRqORIUOG0K1bN8LDw8nNzcXZ2fmuc9irdk1ly8nJwcvLq8Nrl5OTQ2lpKTExMVRWVlJXV4eTk1OL66PT6SgrK6Nnz56YzWZqa2vx8vJqcz57cuxW1wGqq6tJS0vDYDBQU1NDVlYWK1euJC8vj/Lycurr69m7dy+hoaEMGzaMH3/8kQsXLmCxWNizZ889mVHf3Hn9/f1xcXEhPz8fgOzsbEJDQ9FqtQQFBZGTkwPArl277JZTKcWyZcuorKzEZDKxbds2xo8f3+LM7aG4uJg//OEPpKeno9frAcepXVPZHKl2RUVFLFq0CKPRiNFoZN++fUydOtUhatdUtscff9whardp0yb27NlDdnY2CQkJhIWF8fbbb7e4PmPHjmXXrl1AY4MKCgpCq9W2OZ9d3asr/Z3J6tWr1YQJE1R4eLj661//qpRSavfu3Uqv16vw8HD13nvv2fY9dOiQio6OVuHh4Wrp0qXKarXaLde4ceNsvzHV3HkLCwvVM888oyIiItTrr7+uDAaDUkqpoqIiNW3aNBUZGalmzpyprl27ZrdsmzdvVpGRkWr8+PFq5cqVtn1amrmtlixZogIDA9WkSZNs/3300UcOUbvmsjlK7ZRSKiMjQ0VGRqqoqCiVkZHRqhz2+r5rKpsj1U4ppT755BPbb3O1tD4VFRXqlVdeURMnTlTPPfec7e+WI5N5JkIIIdpM3uYSQgjRZtJMhBBCtJk0EyGEEG0mzUQIIUSbSTMRQgjRZtJMRKdRVFTEoEGD2L59+03b//KXv9hWZ20PYWFhnDx5st2Odyc1NTVMnToVvV7P3r1778k572Tt2rUsXry4o2OITkjugBedSpcuXVixYgUjR46kf//+HR2nzQoLC7l69aptuXQhOitpJqJTcXV1ZcaMGcydO5etW7fStWvXmz6flJTEgAEDeOmll277OCwsjKioKA4fPkxlZSW/+93vOHbsGN999x0ajYbMzEzbQnofffQRp06dwmg0MmPGDKZMmQLAV199RWZmJiaTCVdXV+bPn8/w4cNZu3Ytx48f58qVKwwaNIj09PSbcn355ZesW7cOq9WKu7s7CxYswMPDg4ULF1JSUkJMTAzbtm3D1dXV9pi9e/eSmZmJk5MTzs7OvPHGGzz++OMcP36clStXYjQaKS0t5cknn2TZsmUUFRXx29/+lpCQEAoKCrBYLCQkJLBt2zbOnTtHQEAAq1at4vLly8THx/PUU0/x7bffopQiOTmZoKCgmzKXlJSwePFiiouLMZlM6PV6Zs+ejdlsZsmSJRw7dgytVkuvXr14++23cXd3b/evt+hEOvimSSHu2qVLl1RgYKCyWCwqLi5OLV++XCml1Pvvv2+703j+/Pnq/ffftz3mxo/HjRunli1bppRS6rPPPlODBw9WhYWFSimlfv/736vMzEzbfikpKUqpxlk2wcHB6syZM+rHH39UUVFRqry8XCml1JkzZ1RISIiqra1VGRkZKiIi4qY5ONf98MMP6sknn1QXL15USjXekR0SEqKqq6vV4cOHlV6vb/L5/vrXv7bNwPjXv/6l1q5dq5RSKjExUR0+fFgppVRNTY0aNWqUOnnypLp06ZIaOHCg+vLLL5VSSiUnJ6tx48ap6upq1dDQoEJCQlR+fr5tv927dyullNq/f78KCQlRRqNRZWRkqNTUVKWUUvHx8Wrfvn1KKaUaGhpUfHy8+uyzz9TRo0fVhAkTbHeSp6Wlqfz8/Lv6Gor7l7wyEZ1Oly5dWLlyJZMnT2bMmDEtemx4eDjQuLpxjx49GDx4MAB9+vShsrLStt/UqVMB8PX1JSQkhLy8PJydnbly5QovvviibT8nJycuXrwIQGBgoG058RsdPnyY0aNH07t3bwCCg4Px9vamoKDgjivV6vV6Xn31VcaOHUtISAgvv/wyAMuXL+fAgQNs3LiRc+fOYTAYqKurw8vLC61WS1hYmO05DR8+HA8PD6Bx8cDKykp0Oh0PPPCAbV7G2LFjcXZ25vTp07Zz19XVcfToUSorK3nnnXds206dOsWYMWNwdnbm2WefZcyYMURERPDYY4/d7ZdA3KekmYhOyc/Pj9TUVObPn8/kyZNt268v7X3drQOFbnxb7E4L59243LfVakWj0WCxWAgODmbNmjW2zxUXF6PT6fjiiy9wc3Nr8li3LkEOjYs6ms3mO2ZITEzkmWee4eDBg+zcuZMPPviAHTt2MG3aNAYNGsRTTz1FZGSk7a2q68/pxnM1d/wbV/+9nvHGbVarFaUUW7dupVu3bgCUl5fj4uKCu7s72dnZHDt2jMOHD/OnP/2Jl156ibi4uGafi7j/yW9ziU5rwoQJhIaG8re//c227cEHH6SgoABofM//yJEjrTp2VlYW0DijOy8vj+DgYIKDgzl48CBnz54F4Ouvv2bSpEk0NDTc8VjBwcH8+9//5tKlSwC2GRzDhg1r9jFms5mwsDDq6+t5/vnnSUlJ4fTp05SVlXHy5Enmzp1LeHg4P//8MxcvXsRqtbbo+ZWXl3PgwAGg8TqQVqtl4MCBts97eHgQGBhom69eVVXF888/z759+/jnP//Jiy++yPDhw/njH//I5MmTbTUXv1zyykR0aosWLbIt7Q0QHx/P3LlziYiIoFevXowePbpVxzUYDDz99NOYTCYWLVpEv379AFi8eDGvv/66bV5GZmbm/3vh+ZFHHiElJYVXX33VNmJ248aNeHp6NvsYjUbDwoULmTt3LhqNBicnJ5YtW0aPHj2YNWsWTz/9NG5ubvj6+jJixAguXLhgexvtbri4uJCdnU16ejqurq6sX7/+tlcr6enpLFmyhOjoaIxGI1FRUUyaNAmLxcKBAweIiorCzc2NBx54gCVLltz1ucX9SVYNFuIXpqioiOjoaL755puOjiLuI/I2lxBCiDaTVyZCCCHaTF6ZCCGEaDNpJkIIIdpMmokQQog2k2YihBCizaSZCCGEaDNpJkIIIdrs/wBcFV4aNlqxsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_scores_Male = (-1*train_scores_Male)**0.5\n",
    "valid_scores_Male = (-1*valid_scores_Male)**0.5\n",
    "train_scores_mean_Male = np.mean(train_scores_Male, axis=1)\n",
    "train_scores_std_Male = np.std(train_scores_Male, axis=1)\n",
    "valid_scores_mean_Male = np.mean(valid_scores_Male, axis=1)\n",
    "valid_scores_std_Male = np.std(valid_scores_Male, axis=1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_sizes_Male,valid_scores_mean_Male,label='valid')\n",
    "plt.plot(train_sizes_Male,train_scores_mean_Male,label='train')\n",
    "plt.fill_between(train_sizes_Male, train_scores_mean_Male - train_scores_std_Male, train_scores_mean_Male + train_scores_std_Male, alpha=0.3,color=\"g\")\n",
    "plt.fill_between(train_sizes_Male, valid_scores_mean_Male - valid_scores_std_Male,valid_scores_mean_Male + valid_scores_std_Male, alpha=0.3, color=\"b\")\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Male')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "train_scores_Female = (-1*train_scores_Female)**0.5\n",
    "valid_scores_Female = (-1*valid_scores_Female)**0.5\n",
    "train_scores_mean_Female = np.mean(train_scores_Female, axis=1)\n",
    "train_scores_std_Female = np.std(train_scores_Female, axis=1)\n",
    "valid_scores_mean_Female = np.mean(valid_scores_Female, axis=1)\n",
    "valid_scores_std_Female = np.std(valid_scores_Female, axis=1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_sizes_Female,valid_scores_mean_Female,label='valid')\n",
    "plt.plot(train_sizes_Female,train_scores_mean_Female,label='train')\n",
    "plt.fill_between(train_sizes_Female, train_scores_mean_Female - train_scores_std_Female, train_scores_mean_Female + train_scores_std_Female, alpha=0.3,color=\"g\")\n",
    "plt.fill_between(train_sizes_Female, valid_scores_mean_Female - valid_scores_std_Female,valid_scores_mean_Female + valid_scores_std_Female, alpha=0.3, color=\"b\")\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Female')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "train_scores_Marry = (-1*train_scores_Marry)**0.5\n",
    "valid_scores_Marry = (-1*valid_scores_Marry)**0.5\n",
    "train_scores_mean_Marry = np.mean(train_scores_Marry, axis=1)\n",
    "train_scores_std_Marry = np.std(train_scores_Marry, axis=1)\n",
    "valid_scores_mean_Marry = np.mean(valid_scores_Marry, axis=1)\n",
    "valid_scores_std_Marry = np.std(valid_scores_Marry, axis=1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_sizes_Marry,valid_scores_mean_Marry,label='valid')\n",
    "plt.plot(train_sizes_Marry,train_scores_mean_Marry,label='train')\n",
    "plt.fill_between(train_sizes_Marry, train_scores_mean_Marry - train_scores_std_Marry, train_scores_mean_Marry + train_scores_std_Marry, alpha=0.3,color=\"g\")\n",
    "plt.fill_between(train_sizes_Marry, valid_scores_mean_Marry - valid_scores_std_Marry,valid_scores_mean_Marry + valid_scores_std_Marry, alpha=0.3, color=\"b\")\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Marry')\n",
    "plt.legend()\n",
    "\n",
    "\n",
    "train_scores_Unmarry = (-1*train_scores_Unmarry)**0.5\n",
    "valid_scores_Unmarry = (-1*valid_scores_Unmarry)**0.5\n",
    "train_scores_mean_Unmarry = np.mean(train_scores_Unmarry, axis=1)\n",
    "train_scores_std_Unmarry = np.std(train_scores_Unmarry, axis=1)\n",
    "valid_scores_mean_Unmarry = np.mean(valid_scores_Unmarry, axis=1)\n",
    "valid_scores_std_Unmarry = np.std(valid_scores_Unmarry, axis=1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(train_sizes_Unmarry,valid_scores_mean_Unmarry,label='valid')\n",
    "plt.plot(train_sizes_Unmarry,train_scores_mean_Unmarry,label='train')\n",
    "plt.fill_between(train_sizes_Unmarry, train_scores_mean_Unmarry - train_scores_std_Unmarry, train_scores_mean_Unmarry + train_scores_std_Unmarry, alpha=0.3,color=\"g\")\n",
    "plt.fill_between(train_sizes_Unmarry, valid_scores_mean_Unmarry - valid_scores_std_Unmarry,valid_scores_mean_Unmarry + valid_scores_std_Unmarry, alpha=0.3, color=\"b\")\n",
    "plt.xlabel('Number of samples')\n",
    "plt.ylabel('RMSE')\n",
    "plt.title('Unmarry')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'relative importance')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8/vrrmSodRdEyN5MLCUKI7Ekyy2Dq1KmMHj2a6OjoTMs//fRTQkJC2LFjR77bvLTmFGZpSkGF+FJrNKyNoUMQQhRT8lP3fzZu3Mirr76qe+Aanj6InT5SyP3793V/ZzdYsRBCCMORntn/7Nq1i9jYWDw9PXn8+DFJSUmULVuWbdu2AdC2bVvd30IIIYoXk0pm2U2wqe/knKtXrwaeVjL++eef1KpVi0mTJrF06VI2b96sS3Q9e/bEz89P75jq+TtLAUgB0ao1mFvKSB5CiGeZTDLLOMGmWq2mU6dOuLi48Omnn7Jy5UocHR0ZM2YMmzZtok+fPtm2kV7JWLt2bd2yCxcusGDBAho3blxUhyL+R61WY2lpyZkzZ2jSpIkkMiFEjkwmmWU3wealS5f0mpwTnq1kTB/j8cKFCyxbtow7d+7QtGlTJkyYkK+e1sqVK0lJSXnxA3wJjR071tAhCCGMhEkVgGQddio2NlbvyTnTKxkzvp+YmEi9evUYP348ISEhPHnyhG+//baoDkcIIYSeTCqZQeZhp9LS0vSanDO7SkYAW1tbVqxYQZ06dbC0tGTgwIEyOacQQhRDJnOZ8fr166SlpVGvXj3dsFNz5syhZcuW1KhRAwB3d3d++eUXDh48yOLFi4GnVYp//vnnM5WMQUFB9O/fnxMnTuDj4wPI5JxCCFFcmcw3c/qwU6VKlSIuLo579+5RoUIF/vzzT6KjoylTpgzjx4/Hw8Pjmck502VcFhYWhpeXF/PmzWPJkiWUL1+e6OhorK2tdZN96mPw4MFSzfic0gtAhBAiLyZzmbF169a0bt2as2fPYm5uzoABAzhy5AizZs2iX79+ODs78+TJkxwrGR8+fIiVlRUjRozQPVOWPpnnv//+S0pKCu3atePQoUN6JzLxfLQaFYAkMiGE3kzq26Jz585s3LiRChUqsH//fl555RU++OAD9u/fj5eXFwEBAZQsWTLbbc+fP4+iKGzYsIHU1FR2796Nu7s7dnZ2VKtWjVdeeYXLly9z8OBB3N3d9Y4p/NDXKJrkgjrEl0Irjy8MHYIQwsiYVDJ78uQJLi4ufP7556hUKvr27Uvt2rUJDAzMc9u0tDTee+89xo0bx4MHD/Dz88PBwSHH5XXq1CmCIxJCCKEPk0pmjRs3zvRws4+PD6GhobRs2TLPbdu3b0/79u0BqF69Ou+//z7Hjh2jX79+2S6XZCaEEMWHSSWz06dPo1KpdCX2uVUf/vrrrwQHBwPg6+tLlSpVsLOzo2HDhrp1LC0tOXz4cLbLhRBCFB8m9a0cHx/P4sWLCQ4ORqVSERISwvTp07Ndt3fv3vTu3Vv3+pdffmH9+vV8++23xMXFcejQIdasWcPhw4dZvHgxqamppKamEh8fT0hIiN4xNWs7SqoZ80mrUWFuIZNwCiH0Z1LJzNXVlXPnztGtWze0Wi19+vTRe0xFX19frly5goeHB1qtlnHjxlGtWjVcXV1ZsGABlSpVwsLCAltbW5KTpaCjIKk0Wqws/r+wVhKZECK/TCqZAYwaNYpRo0Zl+96hQ4dy3M7S0pIvv/zymeXh4eF07NiRoKAgAL755hv27NnDJ598olc8M/eGkaiRyTlzs8CrtaFDEEIYOZN5zqyw3L9/n0qVKule29vbExMTY8CIhBBCZCXJLA9arRYzMzPda0VRMr0WQghheCZ3mTEhIQFfX1++//57qlevzrFjx5g7dy5arZb69eszc+bMbCfnXL9+PWvWrMHMzAwnJyemT5+OtbU1Fy5c4MSJExw+fBiAypUr89Zbb+kdz5QOzaUAJA9Z75kJIUR+mdQ3yLlz5+jduzeRkZG6ZZMnT2bhwoXs3LmTlJQUtm3b9sx2N2/eZNWqVQQHB7N9+3a0Wi3r1q0DICkpCRsbG1avXk1wcDAxMTG0atWqqA6p0J05c8bQIUgiE0K8MJP6FtmwYQPTpk3D3t5et0yj0ZCQkIBGoyE1NTXbXpK1tTXTpk2jdOnSmJmZ4eDgwN27dwG4evUqlSpVonXr1rz33nt07NgxXz2z4q5JkyYG23eaKvuJUoUQIr9M6jJjdsNWffHFF/j7+1O6dGmqV69Ox44dn1mnWrVqVKtWDYC4uDjWrl3LrFmzdJNzTpw4kVq1ajFx4sR8zxo9ctY2niSpnu+ATNy6uX6GDkEIYSJMqmeWVWxsLF999RU7d+7k2LFjNGrUiFmzZuW4fkxMDP369cPb25vmzZvL5JxCCGEkTDqZnT59GgcHB2rWrIm5uTk9e/YkPDyc8+fP4+npiaenJ5MnTwaeTu7p6+uLl5cXw4cPB+Du3bts2rRJ155MzimEEMWTSX8zOzg4MGfOHB48eICdnR0HDx6kYcOGNGzYMFMhSEJCAoMGDaJBgwZs3ryZzZs307p1awYPHkxQUBArV65Eq9VSqlQpOnXqlK8YFn3mKdWMOUhTabC2sjB0GEIIE2DSyaxOnTqMHDmSvn37YmFhQa1atZgxY8Yz623atIn79+/z6NEjatasCcCePXuoX78+1tbWKMrTETzi4uJ44403ivQYjIlWrcLcUv+hqCSRCSEKikkms4zDVnl5eeHl5ZXr+v3796dly5YkJiby9ttvAzBjxgwiIyOpW7cuP/30EwBbt27lwIEDtGvXTu9YLiybCCnxz3EUxqdJwEpDhyCEeEmZ9D2z/HjzzTd1iSwyMpLdu3djZmYmQ1kJIYQRkGSWxdWrVxk4cCABAQHUqFFDhrISQggjIMksgzNnztC/f3/Gjh2Ll5cXVapUITY2Vvd+bGxspgeyhRBCFA8mec/seURHRzN8+HCCgoJYsGABjRo1olGjRty8eZNbt24RGhrK0qVLc5zsMydOH81+aaoZ81sAIoQQBUWS2f+sWrWKpKQkRo0aRWpqKkOGDKFv377Mnj2bDz/8kNu3b2NnZ5ftCCLiKTXw7BDOQghR+CSZ/c+UKVNITk7Gy8uLgIAAVqxYQfXq1UlLS6NixYoMGTKEbdu25fue2biN04lXJRZS1MXLjwMWGToEIcRLSpJZBtmN7Th//ny8vb2pXr26ASISQgihDykAycXx48eJjo7G29vb0KEIIYTIhfTMskhISCAmJoZ79+6xcuVKTp8+zVtvvYVWq0WlUvHuu+9y7NgxQ4cphBAiA0lmGZw7d44pU6agVqsBWL16te69vXv3Mn78eH755Zd8tflVj2kvTTVjmlqFtVQzCiEMQC4zZpA+uaeFxbNjBgYHB2Nvb89rr71W9IEVU5q0zPO0SSITQhiK9MwySC8AqVKlClWqVNEtj4yM5MaNG+zfvz/fbR4eMx5tvGmOzdjp59V5rySEEEVAemZ6WL9+PX369MHaWp6iEkKI4kiSmR4OHjyY73nMhBBCFB25zJhFxmrG6tWrExcXR3JyMlOmTGHYsGE0b948X+25LphnsgUgmjQVFtZyn0wIYXjSM8vg3Llz9O7dW1fNCBAWFkZCQgJ//PGHASMremqVJs91JJEJIYoL6ZllkF7NGBAQoCsAOX/+PPPmzdNN0Jlf33y1m5Qkdd4rFjOTAn0MHYIQQuhNklkG2Q1nFRAQAPDcyUwIIUThk8uMQgghjJ4kMyGEEEbvpbzM6O/vT1xcHJaWTw9/xowZfPXVV7pl9+/f59KlSwUyUv7wce5GWc2oVmmwtHp2JBQhhCiOXrpkpigKkZGRHD58WJfMsi5r27Yt9erVM3CkhqYFJJkJIYzDS5fMbty4AcDAgQN59OgRPXv2xMXFJdOygQMHZuqVhYWFUa1atXw/Ywawcu4kUpMSCib4IjRm1jJDhyCEEHp76ZLZkydPcHFx4fPPP0elUtG3b18eP378zLLatWvTsmVLQ4crhBBCDy9dMmvcuDGNGzfWvfbx8eHu3bvMnTs307LQ0FCSkpJYunQpSUlJPH78GE9PT+rXr8+sWbMMEboQQogcvHTJ7PTp06hUKt2lRUVRuHz5MidPnsy0zNLSkvfff5/333+fsLAwQkJCmD17tiFDF0IIkQOTTGZz5szh4cOHzJ49mxs3bjBt2jQeP35MpUqV8PLyYtWqVQQHB6NSqQgJCaFDhw6MHDmScuXKUbJkSW7dusXw4cMBOHHiBFOnTiUuLo7KlSszevTofMUyOCDISKsZVVhayXBVQgjjYHLPmZ08eZKQkBDgaQ/r448/ZsiQIWzfvp169epx6dIlWrduTbdu3fD29sbb25vhw4frJuRMS0tj7NixfPTRR6SkpDBp0iRWr15NeHg4Fy5cIDQ01JCHV+i06qdjMkoiE0IYE5PqmT169IiFCxcydOhQLl++zF9//YWNjQ2tWrUCYOjQoTx58oSqVasyatQo3XbJycmkpaVRq1YtYmJiePz4MVqtloiICGrVqkWNGjUA6NKlC3v27KF169Z6x3RpzSnM0pSCPdBC1GhYG0OHIIQQ+WZSyWzq1KmMHj2a6OhoAP755x/s7OyYNGkSly5d4vXXX+fzzz9/ZrsHDx7g7OzMtGnTKFOmDB999BGbNm3CxsaGSpUq6dazt7cnJiamyI5HCCGEfkzmMuPGjRt59dVXdUUcAGq1mvDwcHr37k1ISAg1atTItoijRo0afPPNN9jb21OqVCn8/f0JDQ1Fq9ViZmamW09RlEyvhRBCFA8m0zPbtWsXsbGxeHp68vjxY5KSkrh8+TK1atWiYcOGAHh4eDBixAjOnz/PlClTAHBycqJv375ERkbSoUMH4P+rGatUqUJsbCyHDh1i6dKl3L9/n7JlyxrsGIUQQmTPZJLZ6tWrdX9v2bKF8PBwvvjiC9q3b8/ly5dxdHTk0KFDNGjQgIYNG7Jt2zbd+pcvXyYoKAhnZ2dsbGxYv349Xl5eNGrUiKtXrzJlyhQ2btzI559/TnR0NKGhoXrfN6vn72xU1YxatQZzSxnGSghhXEwmmWWnZMmSfPPNN0yZMoXk5GSqVKmS6eHodI6Ojnz44Ye6Wabd3Nzw8PAAoF27duzbt4+BAwfSunVrgoKCKFmyZFEfSqFTq9VYWlpKIhNCGCWTTGbdu3ene/fuADRq1IhNmzbluY2fnx9+fn7PLDczM8PDw4OoqCjCwsIoVapUpkrIvKxcuZKUlBT9gzeQsWPHGjoEIYR4biZTAFJYNBoNJ0+eJCgoiPXr1xMREaF7jk0IIUTxIMksD3Z2dri4uFChQgVKlixJ+/btiYiIMHRYQgghMjDJy4wFydXVlQkTJnD27Fl69+5NgwYN6N27t97bDx482CgKQNLvmQkhhDGSnlkeGjVqxODBg/n444+xsbHh33//xdvb29BhFThzM+MZpUQIIbKSn+J68PT0ZMGCBWzZsgVfX1+ioqKoWbOmXtuGH/oaRZNcyBG+uFYeXxg6BCGEeG7SM9NDaGgoVatWpXbt2rRv357169cbOiQhhBAZSDLTw+bNm3XPnXXq1IktW7aQlpZm4KiEEEKkk8uMefj33385evQof/31Fz///DOKovDkyRP2799P586dDR2eEEIIJJk9IyEhAV9fX77//nuqV6/O9OnTsbKyoly5cjg5OTF9+nSWLVtGcHCwXsmsWdtRRlHNqNWoMLeQOcyEEMZJLjNmcO7cOXr37k1kZCQAN2/e5NChQ8ycOZPt27ej1WpZt24dfn5+REREcP36dcMGnA8qjTbb5WfOnAGQRCaEMGrSM8tgw4YNTJs2jYCAAACsra1ZtmwZLVu2BMDBwYG7d+9SoUIFzp07p1ebM/eGkagxfNn7Ai/9JxQVQghjI8ksg8DAwEyvq1WrRrVq1QCIi4tj7dq1zJo1yxChCSGEyIVcZtRDTEwM/fr1w9vbm+bNmxs6HCGEEFlIMsvD9evX8fX1xcvLi+HDhxs6HCGEENmQy4y5SEhIoHfv3tjY2LBt2za2bdtGVFQUnp6eTJ06Va82pnRoXiyqGVUaLVYW8ttFCGGa5NstF5s2bSIpKYly5coBkJqaioWFBZ988omBI8s/RWv4IhQhhCgs0jPLxqFDhwDo378//fv31y338/Nj5MiRVKhQQe+2Rs7axpMkVUGHmG/r5j478agQQpgK6Znp6cSJE6SkpODu7m7oUIQQQmQhyUxPwcHBDBgwwNBhCCGEyIZcZswi63BWx44dY86cOVy/fh3rDgTdAAAcZUlEQVRLS0vc3NywtrbWu71Fn3kWiwKQNJUGaysLQ4chhBCFQnpmGWQdzgpg8uTJDBs2jEaNGqFWq9m2bZvhAnwBksiEEKZMklkG6cNZ2dvb65ZpNBoiIyOpXLkyqampxaKXlR9ateGLT4QQorDJZcYMsg5nBfDFF18wZswYSpcuTfXq1enYsWO+2rywbCKkxBdUiPnWJGClwfYthBBFRXpmuYiNjeWrr75i586dHDt2jEaNGsnYjEIIUQxJMsvF6dOncXBwoGbNmpibm9OzZ0/Cw8MNHZYQQogs5DJjFgkJCcTExHDv3j0cHBz48ssv8fLyIi0tDQsLCxo0aJCv9pw+mm3Q+2xatQpzS5mrTAhh2qRnlkF6NaNarQagcuXKpKWl8eTJE8zNzXn06BEODg4GjjJ3aVkKPiSRCSFeBtIzyyDj5JxVqlTh+PHjODs7s3TpUuDpnGYajSZfbY7bOJ14VWJhhJutHwcsKrJ9CSFEcSHJLIOs1Yy3bt3CxsaG0aNHc+PGDf7zn/8wceJEA0UnhBAiJ3KZMRcajYZjx44xZswYtmzZQnJyMsuXLzd0WEIIIbKQZJYLOzs7GjVqRI0aNbCwsMDd3Z2IiAhDhyWEECILucyYC1tbWw4fPkynTp2wtLQkOjqahg0b5quNr3pMK9JqxjS1Cmsp+hBCvGSkZ5aLihUr8uabb2JlZUVaWhrOzs5cu3aNa9euGTo0HU1a5upFSWRCiJeR9MyykT455507dyhfvjxr1qwB4N9//6VHjx6UKVNG77YOjxmPNr7whrPq9PPqQmtbCCGMhSSzPFy4cAFPT0+0Wi3//PMP7u7umQYiFkIIYXhymTEPTk5ObNu2jR07dnD8+HEiIyOlolEIIYoZ6ZnpYenSpezevRt4WuF49uxZA0ckhBAiI0lmeXj8+DHHjh0jJCQErVZLmzZtaN68ud7buy6YV6jVjJo0FRbWUvQhhHi5STLLw61bt6hcuTI9e/ZErVZTtmzZfJfnFybFTK4UCyGEJLNcNG/enHPnzuleR0ZG0rt3b9q3b693G998tZuUJHVhhAfApECfQmtbCCGMhfys19PVq1cZOHAgAQEBvPbaa4YORwghRAaSzPRw5swZ+vfvz9ixY/Hy8jJ0OEIIIbKQy4xZJCQk4Ovry/fff0/16tX54YcfmDdvHlWrVuWPP/6gQ4cOWFrq/7ENH+deqAUgapUGSyuLQmtfCCGMgfTMMkifnDMyMhKAGzdusGTJEkqUKEHp0qXZsWMHrq6u/Prrr4YNNBOtoQMQQgiDk55ZBhkn5wS4cuUK7733HosXLwYgNDSU5cuX07t3b73bXDl3EqlJCYUSL8CYWcsKrW0hhDAW0jPLIDAwkHfeeUf32tHRkXPnzhEdHY1Go2HPnj08ePDAgBEKIYTIjvTMclG7dm3Gjh3Lxx9/TMmSJenYsSPnz583dFhCCCGykGSWi9TUVN566y22bt0KwO7du6lRo4aBoxJCCJGVJLMsEhISiImJ4d69e9ja2tKjRw/s7OwASExMZNy4cflqb3BAUCFXM6qwtJLhrIQQLze5Z5ZBejWjWv10xI6EhASs/pcoNBoNZmZmVKhQwZAhPsNchrMSQgjpmWWUsZqxSpUqVK9endDQUKysrHj48CH+/v6ULVs2X21eWnMKszSlkCKGRsPaFFrbQghhLORnfQZZqxkBrKys2LBhA+3bt6dSpUo4OjoaKDohhBA5kWSmh549exIWFoadnR1Lly41dDhCCCGykGSWi+joaM6cOQOApaUlnTt35sqVKwaOSgghRFZyzywX8fHxjB8/nq1bt2JmZsb48ePp2bNnvtqo5+9cqNWMWrUGc0sZm1EI8XKTZJYLBwcHPvzwQ7p160ZsbCxqtZoePXoYNCa1Wp1poGNJZEIIIcksW4cOHdL97evry/nz5/Hy8iIgICBfI+YDrFy5kpSUlAKLbezYsQXWlhBCmApJZnoIDAw0dAhCCCFyIQUgQgghjJ70zPLh8ePHzJkzhyVLlui9zeDBgwu0ACTrPTMhhBDSM9PbyZMnSUpKMnQYksiEECIbksz08OjRIxYuXEiZMmUMHQpajcrQIQghRLEjP/P1MHXqVEaPHk10dDTh4eH52jb80NcomuQCi6WVxxcF1pYQQpgK6ZnlYePGjbz66qu4uLgYOhQhhBA5kJ5ZHnbt2kVsbCyenp48fvyYpKQkgoKCmDRpkqFDE0II8T+SzPKwevVq/P39iYuL0y3r3Lmz3ts3azuqQKsZtRoV5hYyGacQQmQkySwPiqIQGRnJ4cOH2b59O+Hh4TRq1Mhg8WiwkGvDQgiRhSSzPNy4cQOAgQMH8ujRo3wPNDxzbxiJmoKbnHOBV+sCa0sIIUyFJLM8PHnyBBcXFz7//HNUKhV9+/aldu3atGzZ0tChCSGE+B9JZnlo3LgxjRs31r328fEhNDRUkpkQQhQjcvslD6dPn+bkyZO614qiyCgcQghRzMi3ch7i4+OZMmUKZcuW1b3Oz9iMUzo0L9BqRpVGi5WF/AYRQoiM5FsxD7a2tlhaWqIoCmq1mrS0NMqVK1fkcaSpNACSyIQQIhvSM8tDs2bNOHDgAFZWVty5c4c+ffpgY2Oj9/YjZ23jSdKLj6e4bq7fC7chhBCmSn7m68HKyorFixfTuXNnXFxcqFy5sqFDEkIIkYEkMz2NGDGCkydPEh0dzYYNGwwdjhBCiAxeimS2Y8cOOnXqhJubG2vXrs1xvZiYGN59913d640bN+Lu7k6HDh3w9PTk3XffRaPRcOXKlaIIWwghhJ5M/p5ZTEwMCxcuZMuWLVhbW+Pr60vz5s154403Mq0XGhpKUFAQsbGxumU9evTA3t6exYsX8+WXXzJy5EjMzMxo0qSJ3vtf9JlngVQzpqk0WFtZvHA7Qghhiky+Z3bixAmcnZ0pX748NjY2dOjQgT179jyz3qZNm7ItuW/dujVt2rShd+/eqFQqmjVrlq+BhguKpZm2yPcphBDGwuR7Zvfv36dSpUq61/b29kRERDyzXm7PjjVp0oQjR46wefPmfO//wrKJkBKf7+2eiSFg5Qu3IYQQpsrke2ZarRYzMzPda0VRMr3WR3BwMAMGDCjo0IQQQhQQk09mVapUyXQfLDY2Fnt7ezw9PfH09OTXX3/Ndfu0tDR+//132rZtW9ihCiGEeE4mf5mxRYsWLFmyhLi4OEqVKsW+ffv48ssv+eSTT/Ta/sqVK5QtWxYfHx/UajX9+vXDz0//B5idPppdIAUgWrUKc0uZlFMIIbJj8smscuXKjB49mr59+6JSqfDx8eGtt97Se/vz588TExPDkSNHcq2GLCxpahXWllaSyIQQIhcmn8wAunTpQpcuXfRaN+szZKVKlaJTp06UL18eQFcNqW/PbtzG6cSrEvMXcAY/Dlj03NsKIcTLwuTvmb2o7KohY2JiDBiREEKIrCSZ5aEgqiGFEEIULklmecipGlIIIUTx8VLcM8uPhIQEfH19+f7776levTrW1tZs3bqVU6dO0bFjR44dO8aXX36pd3tf9Zj2QtWM6QUgQgghciY9swzOnTtH7969iYyMBCAlJYV58+Yxfvx4SpYsyS+//EL9+vXzVQ2ZX5q0zHOfSSITQoi8Sc8sgw0bNjBt2jQCAgIAiIiIoFatWvTv35/+/fuzdetWwsLC8tXm4THj0cbrP5xVp59X56t9IYQQkswyCQwMzPRaKhmFEMI4yGXGXEgloxBCGAdJZrmQSkYhhDAOcpkxi4SEBGJiYrh37x6NGjXizz//xNXVlTJlyhAVFUWvXr3y1Z7rgnn5qmbUpKmwsJaiDyGEyA/pmWWQXs2oVqsBKFGiBBUrVqRUqVKkpqbi4+OjKw4pLIqZ/JcIIUR+Sc8sg4zVjFWqVCE5OZlHjx7RuHFjYmJiKF26dL7vm33z1W5SktR6rz8p0Od5QhdCiJeadAMyCAwM5J133tG9fvDgAc7OzgQFBbFhwwZOnz7Npk2bDBihEEKI7Egyy0WNGjX45ptvsLe3p1SpUvj7+xMaGmrosIQQQmQhlxlzceXKFSIjI0lJSWH58uUkJCRQsmRJQ4clhBAiC0lmuVAUhcDAQJKTk/ntt9+YOHEit2/f5sSJE7Ro0UKvNoaPc89XNaNapcHSyuJ5QxZCiJeSXGbMhaOjI/3799cNPuzg4EDp0qVfaODgnKhVT8dklEQmhBD5Jz2zbBw6dEj398CBA7GysmLevHmEhITQtGlT/vOf/+jd1sq5k0hNSshzvTGzlj1XrEIIIaRnlqfLly+zefNmDh8+zNGjRzE3N2fVqlWGDksIIUQGkszycOzYMVxcXKhYsSLW1tZ0796d8PBwQ4clhBAiA7nMmAdHR0cmTpzIwYMHKVGiBCVKlKB169Z6bz84IEive2xqlQpLKxnGSgghnof0zPJgbm6OoijA0+rGBw8e8Prrr+erDa1ak+c6ksiEEOL5STLLw8WLF+natSv79u1j586dDBkyJF8PTl9acwpzS6lQFEKIwiTJLA8NGjTg2LFjPHr0iNTUVA4dOsSDBw8MHZYQQogM5J5ZHlxcXOjevTv+/v6UL18eFxcXzp07Z+iwhBBCZCA9szwkJCTg5ubGjh07WLNmDdbW1tSoUcPQYQkhhMjAqJKZWq3mu+++w93dnU6dOtGhQwe+//57XYHGi/D39ycsLOyZ5VFRUQwbNgy1Wk18fDybNm3C3d1d73br+TvrVQAihBDi+RnVZcbp06fz4MED1q9fT9myZUlISGD48OGUKVMGPz+/Qtmno6Mjbm5udO3aFY1GQ//+/WnSpEm+2pACECGEKFxGk8zu3bvH9u3b+e9//0vZsmUBKF26NFOnTuXatWs8ePCAqVOncu/ePczMzBg7diwtWrRgyZIlxMTEcOvWLe7cuUOPHj34+OOPSUtLY/LkyVy4cIFq1arx8OFD3b6WL1/O7t270Wg0vPvuu4wfPx5PT08GDx7M9u3b2bdvH6tXrzbURyGEECILo0lmERER1KlTh3LlymVaXqdOHerUqcPo0aPx9vamXbt23L9/nz59+rB161bg6VQua9euJT4+nvbt2+Pn58fGjRsB2L17N5GRkXTt2hWA//73v1y4cIFNmzZhZmbG+PHj2b59O02aNOHmzZusXLmS6tWrF+3BCyGEyJXRJDMAMzMz3d979uzhu+++Q6vVYm1tTVRUFDdu3GDx4sXA0/trt2/fBqB58+ZYW1tTsWJFypcvT3x8POHh4fTq1QuA1157jcaNGwNw8uRJIiIi6N69OwApKSlUrVqVJk2aULFiRUlkQghRDBlNMnNycuL69eskJCRQunRpOnbsSMeOHYmKiqJv375otVp++uknypcvD8D9+/epWLEiBw4cyDSclJmZGYqi6P5NZ2n59KPQaDT069ePAQMGAPDkyRMsLCx4+PBhvibmTG87LS3thY+9sKWmpho6hDxJjAVDYiwYEuOLyym+9O/M/Bb2GU0yq1q1Kl27dmXChAnMmjWLsmXLolarOXLkCObm5jg7O7Nu3TqGDRvGtWvX8PPz4+DBgzm25+Liwo4dO3B1dSU6OpqzZ88C4OzszOLFi+nZsyclSpRg+PDheHl50axZs3zFq/rf/GR///338x90Eblw4YKhQ8iTxFgwJMaCITG+uLziU6lU+epAGE0yA/jiiy9YvXo1ffv2RaPRkJiYSPPmzVmxYgU2NjZMnTqVLl26ADB37lxKly6dY1t9+vTh6tWruLu7U61aNRwcHABo27Ytly9fpmfPnmg0Gt577z28vLy4c+dOvmK1tbXFwcEBKyurTJdHhRBC5ExRFFQqFba2tvnazkwpiIe0hBBCCAMyqoemhRBCiOxIMhNCCGH0JJkJIYQwepLMhBBCGD1JZkIIIYyeJDMhhBBGT5KZEEIIoyfJ7Dnt2LGDTp064ebmxtq1a595/9KlS3Tv3p0OHTowefJk1Go1AHfv3sXPz4+OHTvy8ccfk5iYWOxiPHPmDD4+Pnh6etKvX798PzBeFDGmu3jxIk5OToUW34vEeP/+fT788EO6deuGr68vUVFRxS7GqKgo/Pz88PT0xN/f36D/1+kCAgLYsmWL7nVRnTPPG19xOl9yijFdcThf0mWN8YXPF0Xk27179xRXV1fl4cOHSmJiotKlSxfl6tWrmdbp3Lmz8scffyiKoiifffaZsnbtWkVRFOXDDz9Udu7cqSiKoixdulSZO3dusYvR1dVVuXTpkqIoirJx40Zl6NChxS5GRVGUpKQkxdfXV3FwcCiU+F40xn79+inr1q1TFEVR1q1bp4wcObLYxThu3Djd3z///LMyduxYg8V479495aOPPlLeeustZfPmzbrlRXHOvEh8xel8ySlGRSk+50tOMb7o+SI9s+dw4sQJnJ2dKV++PDY2NnTo0IE9e/bo3r9z5w4pKSm8/fbbAHTv3p09e/agUqn4/fff6dChQ6blxSnGtLQ0Ro4ciaOjIwB169YlOjq6WMWYbvbs2fTr169QYnvRGOPi4rh8+TK+vr4AeHt7M2rUqGIVI4BWqyUhIQGA5OTkfI2FV5AxwtNf9O3atcs0k3tRnTPPG19xOl9yijFdcThfcoqxIM4Xoxqbsbi4f/8+lSpV0r22t7cnIiIix/crVapETEwMDx8+pHTp0roR+tOXF6cYra2t8fT0BJ5+0S1dupT27dsXqxgBDh48SEpKCh07diyU2F40xtu3b1O1alVmz57N6dOnqVSpEp9//nmxihFg5MiR+Pr6smbNGlQqFevXrzdIjACDBw8Gnl62S1dU58zzxleczpecYoTic77kFGNBnC/SM3sOWq020+DByv+mlMnr/azrAYU2CPHzxpguLS2NcePGoVar+eijj4pVjLGxsXz33XeFlhwKIka1Ws3FixdxdnZm8+bNtGvXjokTJxarGAEmTJjAjBkzOHr0KNOnT+eTTz7J99QbBRFjTorqnHne+NIVh/MlJ8XpfMlJQZwvksyeQ5UqVYiNjdW9jo2Nxd7ePsf3Hzx4gL29PRUqVCA+Ph6NRpPtdsUhRoDExEQGDx6MWq3mu+++w8rKqljFeOTIER49eqQrXADw9PTUXS4rDjFWqlQJW1tbXF1dAfDw8HjmF6qhY4yLi+PGjRu6nkSHDh2IjY3l4cOHRR5jTorqnHne+KD4nC85KU7nS04K4nyRZPYcWrRowcmTJ4mLiyM5OZl9+/bRqlUr3fvVqlWjRIkSum70tm3baNWqFVZWVrzzzjvs2rULgK1bt2barjjECDB+/Hhq1arF119/jbW1daHE9yIx9ujRgwMHDrBt2za2bdumey+3KX+KOsaaNWtSpUoVQkNDATh8+DANGjQo8PheJMZXXnmFEiVKcPr0aeDpZR9bW1sqVKhQ5DHmpKjOmeeND4rP+ZKT4nS+5KRAzpd8l6sIRVEUZfv27Urnzp0VNzc3Zfny5YqiKMrgwYOViIgIRVEU5dKlS4q3t7fSoUMHZcyYMUpqaqqiKIoSFRWlfPDBB4q7u7sycOBA5dGjR8Uqxr/++ktxcHBQOnXqpHTt2lXp2rWrMnjw4GIVY1aFWZ31IjFev35d+eCDD5TOnTsrvXr1Um7evFnsYjx37pzi4+OjeHh4KL169VL++usvg8WYbsKECZmq3IrqnHme+Irb+ZJdjFkZ+nzJKcYXPV9kPjMhhBBGTy4zCiGEMHqSzIQQQhg9SWZCCCGMniQzIYQQRk+SmRBCCKMnyUyIQrJkyRJmzJiR53oDBw4kLi4OgCFDhnDt2rUX3ndMTIxunLuilvF4hCgqMjajEAZ2/Phx3d8rVqwokDYrV65McHBwgbSVXxmPR4iiIslMiHwICwsjMDAQGxsbEhMT2bx5M8eOHeO7775DpVJRsmRJJkyYQOPGjTNtd/jwYZYtW0ZaWhpxcXF069aNUaNG8dlnnwHQr18/li9fjp+fH4sWLeLHH3+kQYMGDBw4EIB169YRHh7O119/zaFDh/LcX1RUFF26dOGPP/5gyZIl/PPPP8TExBAbG0uDBg1o3rw5W7duJSoqivHjx+Ph4cGSJUu4desW9+7dIzY2FkdHRwIDAyldujRXr15lxowZPHr0CDMzMwYOHEi3bt2e+TzS58pKP57Lly9ne9xhYWEsXLiQGjVqcPXqVdRqNdOnT6dJkyYkJiYyc+ZMzp49i4WFBe3bt2f06NGoVCq++uorfv/9dzQaDfXr12fKlCmFMpKFMEIv+LC3EC+VU6dOKY6OjkpUVJSiKIpy8+ZNxcPDQ4mLi1MURVH+/vtvpWXLlkpiYqKyePFiZfr06YpWq1U++OAD3YgG9+7dU+rVq6f8+++/iqI8HZEh/W9XV1clIiJCOXnypOLh4aHbr4+Pj3L8+PFc95fR7du3lbfffltRFEVZvHix4urqqjx58kRJTk5WmjZtqsyaNUtRFEXZv3+/4ubmpluvVatWSmxsrKLRaJQxY8Yos2fPVlQqldKuXTtl7969uvjfe+895ezZs898HhmPJ7fjPnXqlFKvXj3l4sWLiqIoyqpVqxQ/Pz9FURQlKChIGT16tKJWq5XU1FTFz89POXXqlLJkyRJl9uzZilarVRRFUebPn69MmzbtRf47hQmRnpkQ+fTqq69SrVo14Okltfv379O/f3/d+2ZmZvzzzz+ZXn///fccOXKEnTt3cv36dRRFITk5Ocd9NG/enNTUVM6fP0+pUqWIi4vDxcWFdevW5bi/9Dm1stOiRQvKlCkDPJ2W47333gOejon36NEj3XodO3bEzs4OAB8fH4KCgvD29iY1NRU3Nzfg6SVMNzc3jh49SvPmzTN9HhnlddxVq1alXr16ANSvX5+QkBDg6ZxYn332GRYWFlhYWPDLL78AMG/ePOLj4zlx4gTwdK6zihUr5njM4uUiyUyIfLKxsdH9rdVqcXFx4euvv9Yti46Oxt7env379wOQlJSEl5cX7du355133sHb25sDBw7kOtWKmZkZPj4+bNu2DSsrK3x8fDAzM8t1f7nJOgBu+vxgWVlYWGQ6NnNzczQazTPTeCiKglqtfubzyCiv4844EWj6FEnpsWXcX3R0NCVLlkSr1TJp0iRat24NPB2tPjU1NdfjFi8PqWYU4gW4uLhw/Phxrl+/DkBoaChdu3YlJSVFt86tW7dISEhg1KhRtG3blrCwMNLS0tBqtcDTBJKeGDLy8vLi0KFD7N27l+7du+u9vxdx8OBB4uPj0Wq1bNiwAVdXV15//XUsLS3Zt28f8LRScu/evbRo0SLbNtKPJ6/jzomLiwshISFotVrS0tIYMWIEv//+O++++y5r167VtfH555+zYMGCAjluYfykZybEC3jjjTeYMWMGY8aMQVEULC0t+e6777C1tdWtU7duXdq0aYO7uzvW1tY4ODjwxhtvcOvWLWrWrEnHjh3x9/dnyZIlmdquVKkS9evXR61WU7lyZb339yLs7OwYMmQIDx8+pGnTpgwdOhQrKyu+/fZbZs6cyZIlS9BoNAwfPhxnZ2fCwsKeaSP9eBYtWpTjcec2Vconn3xCYGAgnp6eaDQaOnXqhJubG61atWLOnDl4eXmh0WioV69eoU14KoyPjJovhACePhf38OFDpk6dauhQhMg3ucwohBDC6EnPTAghhNGTnpkQQgijJ8lMCCGE0ZNkJoQQwuhJMhNCCGH0JJkJIYQwepLMhBBCGL3/A7htdYgPFd9jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_Male = RandomForestRegressor(max_depth=1, n_estimators=10).fit(X_train_Male,y_train_Male)\n",
    "f_im_Male = rf_Male.feature_importances_.round(3)\n",
    "ser_rank_Male = pd.Series(f_im_Male,index=X_Male.columns).sort_values(ascending=False)\n",
    "\n",
    "plt.figure()\n",
    "sns.barplot(y=ser_rank_Male.index,x=ser_rank_Male.values,palette='deep')\n",
    "plt.title('Male')\n",
    "plt.xlabel('relative importance')\n",
    "\n",
    "rf_Female = RandomForestRegressor(max_depth=7, n_estimators=30).fit(X_train_Female,y_train_Female)\n",
    "f_im_Female = rf_Female.feature_importances_.round(3)\n",
    "ser_rank_Female = pd.Series(f_im_Female,index=X_Female.columns).sort_values(ascending=False)\n",
    "\n",
    "plt.figure()\n",
    "sns.barplot(y=ser_rank_Female.index,x=ser_rank_Female.values,palette='deep')\n",
    "plt.title('Female')\n",
    "plt.xlabel('relative importance')\n",
    "\n",
    "rf_Marry = RandomForestRegressor(max_depth=3, n_estimators=10).fit(X_train_Marry,y_train_Marry)\n",
    "f_im_Marry = rf_Marry.feature_importances_.round(3)\n",
    "ser_rank_Marry = pd.Series(f_im_Marry,index=X_Marry.columns).sort_values(ascending=False)\n",
    "\n",
    "plt.figure()\n",
    "sns.barplot(y=ser_rank_Marry.index,x=ser_rank_Marry.values,palette='deep')\n",
    "plt.title('Marry')\n",
    "plt.xlabel('relative importance')\n",
    "\n",
    "rf_Unmarry = RandomForestRegressor(max_depth=5, n_estimators=300).fit(X_train_Unmarry,y_train_Unmarry)\n",
    "f_im_Unmarry = rf_Unmarry.feature_importances_.round(3)\n",
    "ser_rank_Unmarry = pd.Series(f_im_Unmarry,index=X_Unmarry.columns).sort_values(ascending=False)\n",
    "\n",
    "plt.figure()\n",
    "sns.barplot(y=ser_rank_Unmarry.index,x=ser_rank_Unmarry.values,palette='deep')\n",
    "plt.title('Unmarry')\n",
    "plt.xlabel('relative importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male Test set RMSE: 4706.875\n",
      "Female Test set RMSE: 5132.161\n",
      "Marry Test set RMSE: 5047.171\n",
      "Unmarry Test set RMSE: 5017.173\n"
     ]
    }
   ],
   "source": [
    "y_predicted_Male = rf_Male.predict(X_test_Male)\n",
    "print('Male Test set RMSE: {:.3f}'.format(mean_squared_error(y_test_Male,y_predicted_Male)**0.5))\n",
    "\n",
    "y_predicted_Female = rf_Female.predict(X_test_Female)\n",
    "print('Female Test set RMSE: {:.3f}'.format(mean_squared_error(y_test_Female,y_predicted_Female)**0.5))\n",
    "\n",
    "y_predicted_Marry = rf_Marry.predict(X_test_Marry)\n",
    "print('Marry Test set RMSE: {:.3f}'.format(mean_squared_error(y_test_Marry,y_predicted_Marry)**0.5))\n",
    "\n",
    "y_predicted_Unmarry = rf_Unmarry.predict(X_test_Unmarry)\n",
    "print('Unmarry Test set RMSE: {:.3f}'.format(mean_squared_error(y_test_Unmarry,y_predicted_Unmarry)**0.5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
